Algolia review: The enterprise giant meets the developer favorite – Meilisearch

Semantic search and retrieval-augmented generation (RAG) are crucial for how today's AI systems handle information and find relevant answers to user queries. 

Think of it this way: semantic search is all about ranking information based on what it means, while RAG takes it further by actively retrieving knowledge to provide the answers users need. 

In this article, we're going to explore:

* What semantic search and RAG are and how they work.
* The contrast between their capabilities.
* The potential for overlap and collaboration, as well as whether or not they can work without each other.
* How to figure out when semantic search is enough, when RAG is essential, and when combining them gives you the best results.
* The pros, cons, and limitations that come with both so that you can evaluate them properly.

By the time you're done, you'll have a clear framework for choosing the best approach for your data and users. 

Ready? Let’s get into it.

## What is semantic search?

[Semantic search](https://www.meilisearch.com/blog/semantic-search) is a technique that helps AI systems understand the intent behind user queries instead of relying on exact keyword matching. It uses embeddings, vector representations, and natural language processing (NLP) to map words by meaning. 

By doing this, semantic search retrieves the most relevant documents even if the query phrasing isn’t perfect. Unlike traditional information retrieval methods, semantic search ranks results based on context and relationships, and produces answers that genuinely make sense. 

Teams can also explore [choosing the best model for semantic search](https://www.meilisearch.com/blog/choosing-the-best-model-for-semantic-search) to refine their search architecture.

## What is RAG (retrieval-augmented generation)?

[Retrieval-augmented generation](https://www.meilisearch.com/blog/what-is-rag) (RAG) combines information retrieval and generative AI to create more factual and contextually relevant responses. It retrieves relevant documents from an external knowledge base or vector database using embeddings before passing them over to the large language model (LLM). 

From there, the LLM uses the retrieved context and ensures that the response accuracy is high and the hallucinations are nonexistent. 

In short, RAG augments the model’s knowledge with real data, making responses more trustworthy and domain-specific.

## How does semantic search work?

[Semantic search](https://www.meilisearch.com/blog/semantic-search) goes beyond keyword matching by understanding the meaning and intent behind those keywords through contextual relationships.

![How Semantic Search Works.png](https://unable-actionable-car.media.strapiapp.com/How_Semantic_Search_Works_77696c3be8.png)

* **Step 1:** Semantic search converts text into embeddings. Each query and document is transformed into dense [vector representations](https://www.meilisearch.com/blog/what-are-vector-embeddings) that capture semantic meaning.
* **Step 2:** Then, it compares embeddings. The system measures cosine similarity or Euclidean distance between the user query and stored vectors to find the most relevant results.
* **Step 3:** Finally, a retriever ranks documents not just by word overlap but also by conceptual closeness, bringing up the absolute best matches.

This process helps users find relevant information even when they aren’t sure of the exact wording needed to get what they’re looking for.

## How does RAG work?

RAG connects advanced retrieval systems with LLMs to produce answers that the users are looking for. 

![A graphic showcasing how RAG works.](https://unable-actionable-car.media.strapiapp.com/How_RAG_Works_b6a617b59f.png)

Let’s see how it works:

* **Step 1:** First, the data is embedded and indexed. Documents from various data sources are split into chunks, converted to vector embeddings, and stored in a vector database.
* **Step 2:** When a user query is submitted, the LLM first evaluates whether it can answer the query on its own. If it needs additional context to provide a satisfactory answer, this is where RAG comes into play.
* **Step 3:** The RAG retriever doesn’t search through the internal vector database. It can access external knowledge bases or even search the internet to find relevant documents.
* **Step 4:** Once RAG provides more context, the LLM uses both the query and the retrieved context to generate more relevant answers. 

As a result of this whole cycle, the answers you get are not only relevant but up-to-date as well.

## What is the difference between semantic search and RAG?

The main difference between semantic search and RAG is what they return. Semantic search only finds specific information, while RAG provides an explanation, enhancing the response to the user query. 

Semantic search works by matching user queries to document vector embeddings using similarity metrics such as cosine or Euclidean distance. It's super-efficient, fast, and great for providing direct answers closely related to the user query.

RAG, on the other hand, feeds retrieved context to a large language model to compose a more elaborate answer. This added step aims to reduce AI hallucinations and improve accuracy.

The table below highlights other key distinctions between the two:

![A table highlighting the key differences between semantic search and RAG.](https://unable-actionable-car.media.strapiapp.com/Semantic_search_vs_RAG_828bd84d6d.png)

You can also explore different [RAG types](https://www.meilisearch.com/blog/rag-types) to understand how architectures vary in retrieval and generation workflows.

## What problems does semantic search solve?

Keyword-based retrieval has always had limitations. Semantic search addresses this limitation by covering a broader domain than just literal keywords. 

Here’s what it solves:

* **Intent detection:** It interprets what the user means, not just what they type. 
* **Disambiguation:** Semantic search removes ambiguity and distinguishes between words with multiple meanings by analyzing their embeddings.
* **Better recall and precision:** It retrieves relevant documents even when queries use different phrasing. 
* **Multilingual and domain-specific results:** Diversity plays a huge role in getting the correct answer to the user. Semantic search adapts across knowledge bases and domain-specific datasets without manual rules.
* **Improved user experience:** Users love semantic search because it ranks relevant information intuitively, helping them find what they need faster.

By combining natural language processing (NLP) and vector embeddings, semantic search ensures users get the results they need.

## What problems does RAG solve?

RAG addresses the main challenges of retrieval and generation by grounding large language models in real data:

* **Hallucinations:** RAG retrieves relevant documents before generation, reducing the risk of made-up or unsupported answers.
* **Outdated knowledge:** It connects to external knowledge bases to keep responses accurate and up to date.
* **Context limitation:** It provides retrieved context beyond the LLM’s memory, thereby improving factual grounding.
* **Complex reasoning:** It enables the model to combine multiple information sources to produce multi-step answers.
* **Trust and explainability:** It cites its sources, ensuring responses are transparent and verifiable.

By combining semantic retrieval with generative models, RAG helps users get precise, context-aware answers rather than vague summaries, using techniques described in our [RAG techniques](https://www.meilisearch.com/blog/rag-techniques) article.

## Can semantic search be used without RAG?

Yes, semantic search can work perfectly well without RAG in many real-world applications. It’s often used when retrieving relevant information is sufficient and generating new text isn’t necessary.

For instance, knowledge bases, search engines, and customer support chatbots rely on semantic search to quickly find the most relevant documents. It’s also used in question-answering systems, e-commerce search, and internal document retrieval, where users want direct access to existing content rather than a generated summary.

Because it operates only on vector embeddings and retrieval algorithms, it’s lightweight, scalable, and cost-efficient.

However, when users expect natural language answers or context synthesis across multiple documents, semantic search alone falls short. That’s when RAG becomes the logical next step.

## Can RAG work without semantic search?

Yes, it can, but it’s not ideal. RAG can technically operate using keyword retrieval or metadata filtering instead of semantic search, but the results are less precise. 

Without vector embeddings or semantic similarity, the retriever may fetch documents that match words but miss intent. And intent is the key in any retrieval.

Some teams use BM25 or hybrid search engines as the retrieval layer for smaller or domain-specific datasets where context is already well structured. This approach simplifies infrastructure and lowers cost, but limits recall accuracy and context coverage.

Skipping semantic search means the model depends more on the generator to interpret data. This is a risky tradeoff that can lead to hallucinations or shallow grounding. 

For most real-world RAG systems, semantic search is the foundation that makes retrieval meaningful before generation is even a thought.

## What are the pros of semantic search?

You already know the accuracy aspect of semantic search. Let’s look at some more pros:

1. **Understands user intent way better:** Semantic search interprets user queries based on meaning, so the retrieved results are better than in plain keyword matching.
2. **Deals with ambiguity like a pro:** Through NLP, it can distinguish between different meanings, such as ‘Apple,’ the company, versus ‘apple,’ the fruit.
3. **Enables scalability:** Semantic searchcan retrieve results quickly and accurately, even across large datasets.

Semantic search is the foundation for both modern search technologies and RAG systems.

## What are the pros of RAG?

RAG provides language models with a way to remain factual, grounded, and context-aware without retraining every time new data arrives. 

Let’s look at some pros of RAG: 

1. **Has access to real-time knowledge:** It retrieves relevant documents from external knowledge sources.
2. **Goodbye hallucinations:** By relying on answers from the retrieved context, RAG minimizes the introduction of made-up or outdated information.
3. **Adapts to various domains easily:** It easily plugs into domain-specific datasets, from medical records to legal documents, without the need to fine-tune the LLM.

Overall, RAG bridges semantic search and generative AI to help teams create applications that not only find data but explain it in natural language.

## What are the limitations of semantic search?

Let’s look at some semantic search limitations. 

1. **Complex queries might result in ambiguity:** Useful as it is, semantic search can falter when queries are too extensive.
2. **Costly if you want to handle massive datasets:** Creating and updating vector embeddings for large datasets demands increasing amounts of resources, which can be expensive.
3. **Potential for low-quality results:** If the stored data is sparse or of poor quality, you might get low-quality results.
4. **Generative reasoning is lacking:** It can’t produce detailed answers, which makes it less helpful in question-answering or chatbot use cases.

These limitations make semantic search excellent for discovery, but not enough when users expect complete, natural-language answers.

## What are the limitations of RAG?

RAG is renowned for its accuracy, but the cost of this accuracy is the added layers of complexity. 

Let’s explore some other limitations of RAG:

1. **Higher latency and cost:** Multiple API calls that enable RAG to be so effective come with hefty charges.
2. **Variable retrieval quality:** If relevant documents are missing or poorly embedded, the LLM will still hallucinate.
3. **Complex infrastructure:** With increasing overhead from vector databases, chunking, and retrieved context pipelines, the RAG system can become really complex.
4. **Difficult to evaluate:** You need continuous assessment of the RAG system to maintain retrieval quality and accuracy.

If you’re working in high-stakes domains that involve knowledge-based systems, RAG is perfect. But it might be a bit too much for simpler applications.

## How is semantic search evaluated for effectiveness?

Semantic search effectiveness is measured by how accurately it retrieves and ranks relevant documents for a given user query. After all, that’s what it is for.

The primary metrics include precision (how many retrieved results are relevant) and recall (how many relevant results were retrieved). 

![A graphic explaining precision and recall, two primary search evaluation metrics.](https://unable-actionable-car.media.strapiapp.com/Precision_and_recall_9d1f66c31e.png)

Other factors, such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (nDCG) help assess ranking quality.

Teams also use user satisfaction surveys or click-through rates (CTRs) to measure success. These evaluations often compare how well embeddings, retrievers, and algorithms capture intent across datasets.

In short, good semantic search is about balancing accuracy, relevance, and scalability.

## How is RAG evaluated for effectiveness?

Evaluating a RAG system requires measuring not only retrieval accuracy but also how well generated answers remain grounded in the retrieved context.

The key metrics for this include factual accuracy, grounding score, and hallucination rate (the frequency with which the LLM adds unsupported information). Teams also track context precision and coverage to assess how the retrieved chunks influence the model’s response.

Other evaluation methods test retrieval quality, latency, and user trust over time. These insights help teams balance precision and performance across different data sources.

For a deeper look into evaluation frameworks, read our guide on [RAG evaluation](https://www.meilisearch.com/blog/rag-evaluation), which breaks down real-world techniques for measuring grounded generation.

## What are the latest trends in semantic search and RAG?

There’s an upward trend in the popularity of both semantic search systems and RAG. Especially now, when AI and LLMs are a core part of almost every enterprise application. 

The two major trends you need to keep up with are:

1. **Integration between LLMs and retrieval systems:** Modern retrieval systems have developed the capability to integrate LLMs and deliver answers that are not only more accurate but also sound conversational and almost human.
2. **Hybrid approach to retrieval:** By combining keywords, vector embeddings, and reranking techniques, we can achieve significant improvements in precision and recall across both structured and unstructured data.

This set of trends, along with some others, contributes to improving RAG daily. In essence, they are all about making AI search engines more intelligent and user-friendly.

## Frequently Asked Questions (FAQs)

The following FAQs should address any remaining confusion about semantic search and RAG.


### Is RAG the same as semantic search?

No. Semantic search ranks documents based on their meaning. RAG retrieves the documents first, and then uses this retrieved context to generate answers the users are looking for. 


### Why is semantic search important for AI?

Semantic search improves how AI understands user queries. It makes the AI interpret the intent behind the search instead of just taking the keywords at face value. Naturally, this brings a significant boost in accuracy and the user experience.


### Why is RAG used in generative AI?

Generative AI needs the right context to generate information. RAG helps LLMs combine retrieved information with the user query to generate the required answers. This significantly reduces AI hallucinations and lays the foundation for real-time AI agents or chatbots.


### What is the role of embeddings in semantic search?

Embeddings are the backbone of semantic search because they convert text into vector representations that capture meaning. They enable the retriever to find semantically related content even when users don’t use the exact keywords in their search. You can think of them as interconnected neural networks.


### What is the role of embeddings in RAG?

In RAG systems, embeddings serve as connectors. They connect the retriever and the generator. They help retrieve the most relevant documents that match a query’s meaning before passing them to the LLM to generate an informed answer.


### How does semantic search compare to vector search?

Semantic search uses advanced algorithms to understand the meaning of words and sentences. It's like a map where similar ideas are close together. This helps it find answers that make sense, even if the exact words aren't there, because it understands what you're trying to find, as detailed in this [semantic vs. vector search](https://www.meilisearch.com/blog/semantic-vs-vector-search) article.

## Harnessing the power of precision

The main goal of most advancements today is to guide AI towards greater precision.

RAG and semantic search are both unique in their own ways, with distinct sets of pros and limitations. However, this does not stop them from being a force to be reckoned with when used together in a hybrid setting.

Semantic search vs. RAG: A side-by-side comparison

Algolia vs Typesense vs Meilisearch: Which search engine powers better developer experiences in 2025?

When working with large language models, achieving accurate, relevant results depends on several factors. 

Two of them are Retrieval-Augmented Generation (RAG) and prompt engineering. 

RAG helps generate better answers by allowing the generative AI model to retrieve information from external knowledge sources, keeping responses fact-based and up to date. 

Prompt engineering, on the other hand, focuses on how you communicate with the AI to produce clear and context-aware answers. 

* RAG adds new knowledge without retraining, whereas prompt engineering improves the model’s use of its existing knowledge.
* RAG works well for research, legal, healthcare, and customer support; prompt engineering is well-suited for summarization, creative writing, Q&A, and data formatting.
* RAG is complex and can be slower, while prompt engineering is cheaper but requires skill.
* RAG requires more setup and maintenance, but prompt engineering is faster and cost-effective.

Choosing the right approach depends on what you want: accuracy, freshness, or speed. Often, combining both gives the best results. Let’s dig deeper into this. 

## What is prompt engineering?

Prompt engineering is the art of crafting clear instructions for large language models (LLMs) to achieve the best possible results. 

Think of it like learning to ask the right questions. A vague prompt might produce a generic answer, while a carefully worded one can give you precisely what you need. 

Prompt engineering is about understanding how the gen AI model interprets instructions and using that insight to guide it toward your desired output. 

## What is RAG (retrieval-augmented generation)?

[RAG](https://www.meilisearch.com/blog/what-is-rag) is a technique that improves LLM outputs by providing them with access to external resources. This could include databases, documents, and APIs. Instead of relying solely on what they were trained on, RAG allows AI models to access additional information when generating answers. 

For instance, we can give an AI model both memory and internet search skills, so that it knows today’s date and searches the internet for the most recent data.

## How does prompt engineering work?

Prompt engineering is about learning how to interact with an artificial intelligence model in a way that generates the best possible response.

As a rule of thumb, it is good practice to provide the LLM with enough context to answer your questions. LLMs are trained to follow user instructions, and they do so within the context they have. 

For example, if you ask an LLM to act like a hip-hop artist, its response will sound like one. Ask it to write like a Nobel Prize–winning author, and it will adapt its responses accordingly.

Finding the right prompt involves experimentation. If the answer from an initial prompt is not accurate, you can refine your prompt to provide more context and make it clearer. You may also check for noise or contradictory statements and eliminate them. 

For example, if you do not have a background in science, instead of asking the model to “Explain photosynthesis,” you could say, “You have over a decade of experience teaching biology. Explain photosynthesis like you are teaching someone in eleventh grade.” The goal is to give the AI enough context and direction to produce relevant responses.

## How does RAG work?

RAG works by combining retrieval and generation so that an LLM can answer using external facts. 

In the retrieval phase, RAG searches external sources to find the most relevant information, and in the generation phase, the model uses that retrieved data to craft an accurate response. 

![How RAG Works.png](https://unable-actionable-car.media.strapiapp.com/How_RAG_Works_e26007ec0c.png)

Here is how the process goes, step-by-step:

1. A user types in a search query. 
2. A retriever searches a document store or database for information that matches the query. 
3. The information is ranked, and the best is selected to form a context. 
4. The original question and the context are sent to the LLM. 
5. The model generates a response grounded in the retrieved information. 
6. Finally, a verifier or re-ranker checks the model's outputs before they are returned. This is optional.

## What are the main differences between RAG and prompt engineering?

The main difference between RAG and prompt engineering is how they help AI models produce better answers. 

Different [RAG types](https://www.meilisearch.com/blog/rag-types) improve accuracy by allowing the model to retrieve real-time data before generating a response. 

Prompt engineering focuses on phrasing and optimizing questions to get the best result from the model’s existing knowledge. 

To sum it up, RAG gets its information externally, while prompt engineering uses information already within the AI model.

![RAG vs. Prompt Engineering.png](https://unable-actionable-car.media.strapiapp.com/RAG_vs_Prompt_Engineering_a00885536d.png)

Now, let’s discuss the benefits and challenges of using RAG and prompt engineering.

## What are the advantages of prompt engineering?

Prompt engineering can help users get the most out of AI tools without requiring additional data. 

Here are some of the advantages:

* **Better control over responses:** You can guide the AI model to produce more relevant outputs by learning how to phrase questions effectively. For example, a marketer can ask ChatGPT to ‘act like an expert marketer in a Fortune 500 company and write an email to promote a new product,’ and receive a good draft email immediately.
* **Works across different tasks:** Prompt engineering can be applied across various sectors. It is helpful for specific tasks such as coding, summarizing reports, writing essays, and even brainstorming ideas. 
* **Saves time and effort:** Making your input prompts clearer reduces the need for multiple iterations. Instead of constantly reworking the answer, a well-crafted prompt helps the AI deliver the desired result in a single go.
* **Encourages creativity and personalization:** Prompt engineering gives users the freedom to set their own tone or style. This makes it an excellent tool for writers, designers, or educators who desire unique results.


## What are the advantages of RAG?

RAG has several benefits that make AI responses more helpful in real-life situations.

* **Improves accuracy:** RAG reduces the risk of errors by pulling information only from verified external sources.
* **Access to up-to-date information:** RAG can retrieve current information from live databases or documents. This makes it perfect for fast-changing topics, such as the news.
* **Better context understanding:** RAG provides the AI model with additional background information before generating an answer, helping it better understand the question and respond more accurately.
* **Flexibility across domains:** Because it can connect to various data sources, RAG can easily adapt to resource-intensive, specialized fields like healthcare, agriculture, or finance while still delivering insights tailored to each domain.
* **Reduced AI model training costs:** Since RAG relies on retrieval rather than constant retraining, organizations can update knowledge from data sources rather than rebuilding the entire model for each process.

## What are common challenges with prompt engineering?

Prompt engineering is a powerful skill, but it is not always straightforward. Generating the perfect response from AI often takes creativity and practice. 

Here are some common challenges:

* **Inconsistency in results:** Just a slight change in wording can completely alter the AI’s answer. Also, because AI is non-deterministic, what works once may not work the same way next time, making it hard to achieve consistent responses.
* **A need for expertise:** Crafting a strong prompt requires some level of domain knowledge and an understanding of how the AI model interprets instructions. Newbies may give generic prompts, leading to generic AI responses.
* **Can be time-consuming:** Refining your prompts through trial and error can take a long time, especially when dealing with complex tasks. If not properly managed, this can slow down your workflow or even reduce your productivity.
* **Model limitations:** AI systems only know the training data on which they were built. Therefore, even the best prompt cannot fix a model whose knowledge base is lacking. Such situations lead to hallucinations or outdated responses.

## What are the common challenges with RAG?

Some challenges can make RAG implementation difficult, even though it greatly improves accuracy and relevance. 

Here are some of them:

* **System complexity:** RAG can be complex to set up and maintain, especially for non-technical teams. It requires [connecting a retriever and a database to the LLM](https://www.meilisearch.com/blog/how-do-you-search-in-a-database-with-llm).
* **Integration overhead:** Integrating a RAG system into an existing application or workflow often requires extra resources. You have to set up the RAG infrastructure while maintaining the external data sources at the same time.
* **Latency and speed issues:** Balancing accuracy with performance can be challenging with RAG. Since it searches and retrieves data before generating a response, it can be slower than the traditional models, especially when handling large datasets. 
* **Information retrieval error:** Sometimes a RAG system retrieves the wrong documents, leading to answers that sound confident but are based on inaccurate data. This makes proper data indexing and filtering critical when setting up the RAG system.

## What are examples of prompt engineering use cases?

Prompt engineering can be applied in many real-world situations to make AI tools more valuable and efficient: 

* **Question answering:** Businesses use prompt engineering to make chatbots and help desks smarter. With the right prompt, an AI agent can provide clear and accurate responses to customer or employee questions.
* **Text summarization:** It is excellent for turning long text into short but meaningful takeaways. For instance, you can say, ‘Summarize this report in three simple points,’ and save hours of reading time.
* **Data conversion:** Prompt engineering can help you quickly clean or restructure information. For example, changing your raw notes into a professional report.
* **Creative writing:** Writers use prompts to get innovative ideas, explore storylines, or overcome creative block.

## What are examples of RAG applications?

[RAG tools](https://www.meilisearch.com/blog/rag-tools) are powering many of the apps we use today. Here are a few ways RAG is being used in the real world:

* **Customer support systems:** Many customer service chatbots now use RAG to fetch real answers from company manuals. So when you ask your bank’s chatbot about your account details, it gives you the exact information instead of a generic reply.
* **Legal research:** Law firms use RAG to review thousands of case files and contracts in seconds. A lawyer can ask about precedents for a case and instantly get relevant examples instead of manually searching the archives.
* **Healthcare and medical support:** Doctors and researchers use RAG to find the latest studies, treatment guidelines, and patient data. This enables them to make more informed medical decisions.
* **E-commerce product comparison:** RAG is used by e-commerce companies to compare products. It retrieves product specs, reviews, or prices from the database and generates personalized recommendations.

## Can RAG and prompt engineering be combined?

Yes, RAG and prompt engineering work better together. 

For example, when someone asks, ‘Summarize this customer’s issue in three bullet points,’ a RAG setup might pull a customer’s record from the database, and a good prompt allows the LLM to summarize only the issues returned. 

In legal work, RAG can find relevant cases, which are then combined with a prompt that says, ‘Compare these rulings and highlight the key differences.’ This provides a concise, actionable comparison rather than a long list that might be difficult to interpret. 

In healthcare, RAG retrieves patient history and recent medical studies, while prompts ensure the results follow clinical reporting standards.

RAG handles the ‘what’ (or the facts), and prompt engineering handles the ‘how’ (or the presentation). When used together, they make answers both relevant and accurate.

## What are the costs of RAG vs. prompt engineering?

The costs of setting up RAG and prompt engineering are different. 

RAG can be expensive to implement because it requires additional infrastructure (such as a database, a retrieval system, and integration tools). There is also a maintenance cost for updating data sources, handling storage, and ensuring the retrieval system stays accurate and fast. 

Prompt engineering, on the other hand, is far cheaper because it mainly involves using your time and skill. You have to learn how to design effective prompts and also test them for consistent results. There is no need for new servers, APIs, or databases, making it suitable for quick, low-cost projects.

RAG requires more upfront investment but delivers higher accuracy, while prompt engineering is cost-effective and easily scalable for smaller applications.

Want to decide whether to use RAG or prompt engineering? Let’s see the factors you should consider when choosing.

## How do you choose between RAG and prompt engineering?

Choosing between RAG and prompt engineering depends on what you need from your system and the kind of resources you have. 

Here is a simple decision guide to use:

* **Check if you need fresh data:** RAG is the way to go if your answers must include the latest information. But if you are working with general knowledge that doesn't change often, prompt engineering should be sufficient.
* **Weigh your accuracy and reliability needs:** For fields like healthcare, law, or finance, where mistakes can be costly, RAG is safer because it cites real sources. Prompts alone cannot always guarantee accuracy. 
* **Consider your time, budget, and skills:** RAG takes more setup and maintenance, while prompt engineering requires creative thinking and practice. If you have limited resources, then go with prompt engineering.
* **Balancing performance and workflow:** RAG systems can be slightly slower since they search external data before responding, but the answers are usually more accurate. Prompt engineering is quicker and is perfect for lightweight applications where speed matters more than depth.

## How does RAG compare to fine-tuning?

The main difference between [RAG and fine-tuning](https://www.meilisearch.com/blog/fine-tuning-vs-rag) is that RAG pulls in fresh information from external sources when needed, while fine-tuning actually trains the model to learn that knowledge internally.

With fine-tuning, you take a pre-trained model and feed it domain-specific data (such as legal documents or customer records) so that it learns the patterns and language of that field. This makes it faster and consistent. 

The limitations are that it is expensive to train, requires lots of clean data, and must be redone whenever new information comes in.

RAG, on the other hand, does not retrain the model. Instead, it connects to a database or document store and retrieves the most relevant information whenever a question is asked. This ensures the answers are up to date and adaptable to new topics.

## How does prompt engineering compare to fine-tuning?

The main difference between prompt engineering and fine-tuning is that prompt engineering focuses on how you ask the model your question to get the best response, while fine-tuning changes what* *the model actually knows.

With prompt engineering, you are not retraining anything. You are instead crafting the proper instructions to guide the model’s behavior. It is fast, affordable, and flexible, making it perfect for tailoring outputs to specific needs. 

The catch is that results can vary greatly depending on how well the prompts were written.

Fine-tuning, on the other hand, takes things deeper. You train the model on new data so it permanently learns that knowledge. This gives you consistency and expertise but comes with higher costs, more setup time, and the need to continuously retrain to stay up to date.

## RAG vs. prompt engineering: and the winner is...

The reality is that both techniques shine in their own way. 

RAG is your go-to tool when accuracy and up-to-date information matter most, since it can retrieve from live data sources. Prompt engineering is the fastest, most flexible way to generate outputs without retraining any model.

If you are building something that needs freshness, go with RAG. For quick, creative control, prompt engineering takes the lead. 

RAG vs. prompt engineering: choosing the right approach for you

Algolia vs Elasticsearch (vs Meilisearch): Which search solution actually fits your needs in 2025?

GraphRAG vs. Vector RAG: Side-by-side comparison guide

Another [Launch Week](https://www.meilisearch.com/launch-week-q3-2025) is in the books. We shipped five major updates this week, designed to solve real problems our users told us they were struggling with. Each one of them makes your search experience more powerful, more flexible, and easier to work with. 

Here's what we built:
##  Meilisearch Chat*: we brought conversational AI to Meilisearch

Search is becoming a conversation. Your users no longer want to scroll through results - they want to ask a question and get an answer. But building conversational AI has meant juggling LLM calls, vector databases, reranking logic, and weeks of infrastructure work. 

Our new `/chat` endpoint brings complete RAG capabilities directly to your Meilisearch index. One API call handles everything: understanding the query, retrieving relevant data, and generating accurate responses. 

Key features:

- **Conversational AI for search:** your users can ask natural-language questions and get precise, contextual answers directly from your Meilisearch index.
- **Built-in RAG capabilities:** retrieves relevant information, contextualizes it, and generates accurate responses - no separate vector database, reranker, or AI service required.
- **Native integration:** runs directly on your Meilisearch instance as a single API endpoint.

_*This feature is in open beta._

[See the demo → ](https://supportbot.meilisearch.com/)

[Explore the docs → ](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat)

[Read the full announcement and start building with `/chat` →](https://www.meilisearch.com/blog/meilisearch-chat)

##  Advanced geosearch

Location-based search is powerful, but until now, Meilisearch only supported [basic shapes](https://www.meilisearch.com/blog/powering-bildhistoria-discovery) and points on the map. If your use case involved complex shapes, delivery zones, city boundaries, or irregular service areas, you had to work around it.

We've added full GeoJSON support with polygon and custom shape filtering. It allows to define a complex geographic boundary and search within it with precision. It's built on the same fast, filterable foundation you're used to, just way more flexible.

Key features:

- **Advanced shapes search:** search within polygons, multi-points, and complex shapes
- **Fine-grained filtering:** mix geo queries with text relevance and filters
- **Faster performance:** optimized for real-time map and delivery use cases

[See the demo → ](https://chantiers-paris.vercel.app/)

[Explore geosearch with polygons in the docs →](https://docs.meilisearch.com/)

##  Enhanced multi-modal: integrated image search

Image search has been possible in Meilisearch, but it required you to generate embeddings externally and inject them manually. For a lot of teams, that friction was enough to shelve the idea entirely.

Now it's built in. Provide images in base64 format, select an embedder (like CLIP), and Meilisearch handles the rest-embedding, indexing, and search. Support text-to-image queries, image-to-image queries, or both. Whether you're building [product catalogs](https://www.meilisearch.com/blog/Agora), [media libraries](https://www.meilisearch.com/blog/motionelements), or visual discovery tools, you can ship image search without the setup headache.

Key features:

- **Integrated support:** image-to-image and text-to-image search now built directly into Meilisearch
- **Multi-modal queries:** mix image, text, and embedding inputs seamlessly
- **Simple setup:** everything runs through a single API and index with flexible configuration

[See the demo → ](https://multi-modal.meilisearch.com/)

[Explore how multi-modal search works in our docs →](https://www.meilisearch.com/docs/learn/ai_powered_search/image_search_with_multimodal_embeddings)

##  Search personalization: results that adapt to each user

Not everyone searches the same way. A user shopping for running shoes shouldn't see the same results as someone looking for dress boots - but most search engines treat every query identically.

Search personalization lets you rerank results based on user context: preferences, behavior, past interactions. Powered by Cohere Rerank 3.5, it works with a simple `personalize` parameter. You provide the user context, Meilisearch adjusts the ranking, and your users get results that actually match their intent.

Key features:

- **Personalization API:** individualize search results based on behavior
- **Cohere 3.5 embeddings:** accurate, privacy-safe personalization
- **Lightweight integration:** add personalization without reindexing

[See the demo →](https://personalization.meilisearch.com/) 

[Talk to our team to get access to personalized search →](https://meet.meilisearch.com/meetings/cloud/presentation)

##  Resource-based pricing: predictable costs that scale with you

Usage-based pricing works when your workload is unpredictable. But as you scale, you want to know exactly what you're paying for: compute, memory, and storage, ideally, without surprise bills tied to search volume.

We've introduced resource-based pricing alongside our existing usage-based model. Choose your dedicated resources (CPU and RAM) from predefined tiers, and your costs stay predictable as you grow. You can see real-time resource usage, upgrade when you need to, and only pay for what you allocate. 

Key features:

- **Resource-based Pricing:** transparent, usage-based costs
- **Predictable scaling:** pay as you grow with your data
- **Simple migration:** no changes needed to your setup

[Learn about resource-based pricing  →](https://www.meilisearch.com/blog/resource-based-pricing) 

[Get started now →](https://www.meilisearch.com/cloud)

---

## What's next

These releases aren't just features, they're the foundation for where we're taking Meilisearch. Conversational AI, multi-modal search, and personalization: it's all pointing toward search that understands context, not just keywords.

If you're building with Meilisearch or thinking about it, now's a good time to dig in. We're here if you need help.

That's a wrap: Meilisearch Launch Week, October 13-17th 2025

If pricing makes you nervous, you build less.

We’ve seen that first‑hand. When your bill swings with every spike in documents or searches, teams hesitate to ship, and finance can’t forecast.

Our philosophy towards building Meilisearch – from our [dual licensing model](https://www.meilisearch.com/blog/enterprise-license) to how we price – has always centered on giving our customers maximum flexibility.

That’s why we’re introducing resource-based pricing: a developer-first model that stabilizes spend, aligns pricing with actual infrastructure, and provides clear controls over your performance and infrastructure.

## Why we’re adding another pricing model

For a while, usage‑based pricing looked like the obvious path. It matched the serverless zeitgeist and mapped neatly to “documents” and “searches.” But reality didn’t fit the abstraction:

- **Unpredictability drove churn**. Teams with a large amount of data and relatively few searches were penalized. Bills rose in ways that were hard to anticipate.
- **Identical “units” did not have identical costs**. A million tiny product records and a hundred thousand large PDFs do not consume the same resources – yet usage pricing treats them the same.
- **We were over‑optimizing for plan limits**. Internally, we found ourselves hand‑tuning infrastructure to fit plan boxes rather than letting customers scale the resources they needed.

Customers (especially developers and CTOs) told us what they wanted instead: price stability, performance control, and transparency on what drives cost.

## What “resource‑based” means

With resource‑based pricing, you select dedicated compute for your project – and you pay **only** for those resources.

![Resource-based pricing](https://unable-actionable-car.media.strapiapp.com/resource_based_pricing_414c8e98aa.png)

- **Compute**: choose the CPU and RAM you want. Those resources are dedicated to your project via predefined tiers (e.g., XS through 4XL self‑serve; larger tiers via Sales).
- **Storage**: storage auto‑scales and isn’t customizable at the moment.
- **No caps on “documents” or “searches.”** Your resources define your limits and performance – scale them up or down as your needs change.

You can upgrade resources self‑serve. At the moment, downgrades are available via support, but very soon you’ll be able to do them from the Cloud interface.

Billing is at the team level. You can run mixed billing in the same invoice (usage‑based projects alongside resource‑based ones), and you can upgrade/downgrade resources as you go (downgrades via support for the moment, as mentioned above).

This mechanism is the same for both self-serve and enterprise. The Enterprise level then adds SLAs, SSO, support/SLOs, volume discounts, and optional add‑ons – not different machines.

Why this is better for teams needing to scale:

- **Predictability**: stabilize spend as you grow – no more surprise overages tied to abstract units.
- **Control**: developers can dial resources to match performance goals – whether that’s sub‑second search or faster indexing.
- **Transparency**: your bill tracks real cost drivers (CPU, RAM, storage), making budgeting (and explaining costs) far easier.

It also improves activation. If indexing or search feels slow, you can bump resources for a few cents per hour to validate your use case, then tune back once you’ve optimized.

## The developer experience: monitor, tune, repeat

Alongside the pricing model, we’re rolling out a new **Infrastructure** tab in the [Cloud UI](https://cloud.meilisearch.com/) so you can see what’s happening under the hood:

![The new Infrastructure tab allows you to monitor resources](https://unable-actionable-car.media.strapiapp.com/infrastructure_tab_278a604bfd.png)

The new interface allows you to monitor CPU, memory, and storage usage live and take action proactively to avoid performance issues.

Coming soon are also alerts and recommendations. You’ll be able to set up proactive alerts when resource usage reaches certain thresholds (e.g., 80/90/95%) to avoid outages and slow performance.

Wherever possible, we’ll suggest optimizations first (configuration and data‑model tips), then scaling guidance if you still need more headroom.

## How does this fit with usage‑based (we’re keeping both)

This isn’t an either/or ideology. It’s a toolbox:

- Start with usage‑based if you want near‑zero friction and the simplest mental model for a quick evaluation.
- Switch to resource‑based when predictable spend and performance control become more important than per‑unit simplicity.

We’re not forcing migrations. Existing plans are grandfathered. Going forward, when you create a project, you’ll be able to choose usage‑based or resource‑based in the flow – and mix them across a team as your portfolio grows.

## Add‑ons and future flexibility

Some capabilities sit above pricing mode:

- Team‑level add‑ons: SSO, SOC 2, analytics, merchandising
- AI services as first‑class add‑ons on a single Meilisearch bill: embeddings (by size), LLM tokens, re‑ranking models
- Horizontal scale via sharding (multiple machines per project) for advanced workloads

Bandwidth billing and alerting are planned future improvements.

## What to expect at launch

- Fully functional backend: applicable to any new or existing project; resource upgrades are self‑serve; downgrades via support
- Team‑level billing with mixed cycles: usage‑based and resource‑based projects on one invoice
- New Infrastructure tab in the dashboard to monitor CPU, memory, and storage
- Self‑serve and enterprise both supported; enterprise layers on support and governance – not different pricing mechanics

We are confident this new pricing model will lead to lower churn and faster activation because customers can plan with confidence and validate performance without friction.

## Choosing the right model (quick guide)

Choose usage‑based if:

- Business stakeholders lead the evaluation
- Traffic is modest and bursty
- You want the simplest on‑ramp

Choose resource‑based if:

- You’re a developer/CTO who needs predictable spend
- You want explicit control over performance via CPU/RAM
- Your data shape doesn’t map cleanly to “documents/searches”

## How to try it

- New projects: pick “Resource‑based” when creating a project.
- Existing projects: talk to our support team to switch to resource‑based pricing.
- Then, visit the Infrastructure tab to monitor resource usage and adjust CPU/RAM as needed. If you want help optimizing before scaling up, talk to us – we’re happy to review settings and tradeoffs with you.

<Callout intent="note">

Free trials are only offered for usage-based plans, if you want to start a resource-based project, you’ll need to purchase a subscription from the get-go. Of course, you can also start a trial on usage-based to verfy that Meilisearch is the right fit for you and then talk to Support to get migrated to a resource-based plan.

</Callout>

## Our goal

Pricing should reflect reality, not get in your way. Resource‑based pricing puts control back in developers’ hands and makes your bill as predictable as your architecture.

I’m excited to see what you build when pricing is finally one of the simplest aspects of running a search engine.

Scale with confidence: Introducing resource‑based pricing

We're kicking off [Launch Week](https://www.meilisearch.com/launch-week-q3-2025) with something we've been excited to share: Meilisearch Chat, a new way to make your data conversational.

For the first time, you can turn your existing Meilisearch index into an AI-powered chat experience – without complex infrastructure, separate vector databases, or months of custom development.

## Search is becoming a conversation

Your users and buyers don't want to search anymore – they want to ask. They expect apps, documentation, and internal tools to understand natural language and give them direct answers, not endless lists of results.

Until now, that's been really hard to deliver. Traditional conversational AI required juggling multiple LLM calls, vector databases, reranking pipelines, and endless tuning. The complexity alone made it expensive and slow, often taking months just to get a working prototype.

Meilisearch Chat changes all of that.

## One API call, complete conversational AI

The new `/chat` brings AI-native search to your Meilisearch data through a single API endpoint. It handles the entire Retrieval-Augmented Generation (RAG) workflow, from understanding the query to generating the response, automatically.

Here's what you get:

**Conversational AI for search** – Users ask natural-language questions and get precise, contextual answers from your Meilisearch index.

**Built-in RAG capabilities** – Retrieves relevant information, contextualizes it in your factual data, and generates accurate responses. No extra vector database, reranker, or separate AI service required.

**Native integration** – Runs directly on your Meilisearch instance with a single, OpenAI-compatible endpoint.

Less infrastructure. Faster implementation. No compromise on quality or accuracy.

## Why this matters for your business

**Get to market faster**

Meilisearch Chat turns months of AI integration work into days. You can prototype conversational experiences immediately, test them with real users, and iterate quickly without needing a dedicated AI team.

**Lower costs, less complexity**

We've streamlined the entire RAG pipeline, so you don't need separate LLM orchestration, reranking logic, or vector storage. Fewer moving parts means lower maintenance costs and less things that break.

**Better user experiences**

Whether it's customer support or internal knowledge bases, your users get direct, reliable answers in context – not just a list of links. That means faster resolution times, higher engagement, and happier users.

## Built for real-world use cases

**Retail & e-commerce** – Help shoppers find exactly what they need through natural conversation.

**Customer support** – Let users ask questions in plain language and get answers directly from your documentation or help center.

**Internal knowledge bases** – Turn your company documentation into a chat assistant that answers questions instantly.

**Developer docs** – Add conversational search to your API or SDK documentation so developers can find what they need faster.

## Simple to implement, easy to scale

If you're already using Meilisearch, adding conversational capabilities is straightforward. The `/chat` endpoint is [OpenAI-compatible](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat#configure-a-chat-completions-workspace), which means it works with your existing LLM setup and fits right into your current workflows.

You can start with your existing indexes – no need to rebuild or re-embed your data.

## A new approach to AI-powered search

With Meilisearch Chat, we're making it simpler to bring conversational AI to your data. Instead of managing multiple services and complex pipelines, you get a complete conversational search experience through one endpoint.

This isn't just another feature – it's a new foundation for more natural, human-like interactions with your data.

**Ready to make your search conversational? Explore the [docs](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat) for how to get started and [try /chat today](https://www.meilisearch.com/cloud)**

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