What is RAG? (Retrieval Augmented Generation) | Clarifai Blog

Generative AI continues to astound users with fluent language and creative outputs, yet large language models (LLMs) still make mistakes. Traditional models are trained on data up to a cutoff date and can hallucinate facts when they don’t know the answer. Retrieval‑augmented generation (RAG) was proposed to solve this problem by grounding a model’s answers in external knowledge sources. In a RAG pipeline, a user’s query triggers a search over a knowledge base, relevant documents are retrieved and combined with the user’s prompt, and the LLM generates an answer that cites its sources. This simple idea makes answers more accurate, builds user trust and is cheaper to implement than retraining a model.

Recent surveys show RAG is no longer experimental. Menlo Ventures’ 2024 report found that 51% of enterprise AI systems use RAG, up from 31% in 2023. Only 9% of production models are fine-tuned, and agentic architectures (systems that coordinate multiple agents or tools) already account for 12% of implementations. A separate survey of 300 enterprises by K2view discovered that 86% of organizations augment their LLMs with frameworks like RAG and only 14% rely on generic models. These numbers indicate that retrieval‑augmented generation has become the dominant design pattern for enterprise AI.

Quick Summary: What is retrieval‑augmented generation (RAG), how does it work, and why is it important for generative AI?

RAG augments language models with retrieval systems to fetch relevant external information, reducing hallucinations and keeping answers current.

Why do we need RAG?

LLMs trained on massive text corpora can generate fluent responses, but they often exhibit hallucinations—fabricated statements not grounded in source material—and rely on training data that quickly becomes stale. WEKA explains that before RAG, models depended on static knowledge embedded at training time, making them prone to errors on real‑time or specialized queries. Because LLMs like GPT‑4 have knowledge cut‑offs, they struggle to answer questions about recent events or domain‑specific facts. RAG addresses these issues by allowing models to search external sources and incorporate the retrieved evidence into their responses.

Market research underscores the growing importance of RAG. Menlo Ventures’ 2024 report noted that 51% of generative AI teams use RAG, up from 31% the prior year; only 9% rely on fine‑tuning and 12% on agentic architectures. A survey by K2view found that 86% of organisations augment LLMs with RAG frameworks, while only 14% use plain LLMs. Adoption varies by sector: Health & Pharma (75%), Financial Services (61%), Retail & Telco (57%), and Travel & Hospitality (29%). These statistics signal that RAG is becoming the default strategy for enterprise AI.

Quick Summary: “Why isn’t a large language model alone enough? What problems does RAG solve?”

LLMs hallucinate and quickly become outdated. RAG solves these issues by retrieving relevant documents at inference time. Adoption studies show over half of generative AI teams already use RAG, and 86% of organisations augment LLMs with RAG.

How Does RAG Architecture Work?

This article focuses on what's known as "naive RAG", which is the foundational approach of integrating LLMs with knowledge bases. We'll discuss more advanced techniques at the end of this article, but the fundamental ideas of RAG systems (of all levels of complexity) still share several key components working together:

• Orchestration Layer: This layer manages the overall workflow of the RAG system. It receives user input along with any associated metadata (like conversation history), interacts with various components, and orchestrates the flow of information between them. These layers typically include tools like LangChain, Semantic Kernel, and custom native code (often in Python) to integrate different parts of the system.

• Retrieval Tools: These are a set of utilities that provide relevant context for responding to user prompts. They play an important role in grounding the LLM's responses in accurate and current information. They can include knowledge bases for static information and API-based retrieval systems for dynamic data sources.

• LLM: The LLM is at the heart of the RAG system, responsible for generating responses to user prompts. There are many varieties of LLM, and can include models hosted by third parties like OpenAI, Anthropic, or Google, as well as models running internally on an organization's infrastructure. The specific model used can vary based on the application's needs.

• Knowledge Base Retrieval: Involves querying a vector store, a type of database optimized for textual similarity searches. This requires an Extract, Transform, Load (ETL) pipeline to prepare the data for the vector store. The steps taken include aggregating source documents, cleaning the content, loading it into memory, splitting the content into manageable chunks, creating embeddings (numerical representations of text), and storing these embeddings in the vector store.

• API-based Retrieval: For data sources that allow programmatic access (like customer records or internal systems), API-based retrieval is used to fetch contextually relevant data in real-time.

• Prompting with RAG: Involves creating prompt templates with placeholders for user requests, system instructions, historical context, and retrieved context. The orchestration layer fills these placeholders with relevant data before passing the prompt to the LLM for response generation. Steps taken can include tasks like cleaning the prompt of any sensitive information and ensuring the prompt stays within the LLM's token limits.

The challenge with RAG is finding the correct information to provide along with the prompt!

Indexing Stage—Organizing Your Knowledge Base

Before querying is possible, raw data must be prepared:

• Data ingestion: Collect documents from databases, webpages, PDFs or other sources. Clean and normalise text, extract metadata and remove irrelevant sections.

• Chunking: Split documents into manageable pieces (tokens or sentences). Choosing the right chunk size is critical—small chunks reduce noise, while larger chunks provide context.

Different chunking strategies exist. Standard approaches divide text into fixed token windows, but advanced techniques like sentence‑window retrieval and auto‑merging retrieval improve context handling. Sentence‑window retrieval embeds each sentence with sliding windows to give the retriever multiple candidate contexts. Auto‑merging retrieval merges adjacent chunks into parent nodes and uses a hierarchical index to retrieve the parent when its children are retrieved.

Quick Summary: What happens during the indexing phase of a RAG pipeline?

The indexing phase ingests and cleans data, splits it into chunks, encodes each chunk into vectors and stores them in a vector database. Advanced chunking methods like sentence‑window retrieval and auto‑merging retrieval preserve context.

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Querying Stage

When a user asks a question, the system proceeds through these steps:

• Query encoding: The input is converted into an embedding using a language model such as BERT or GPT.

• Retrieval: The system searches the vector database or other repositories for top‑N chunks relevant to the query.

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• Ranking and filtering: The retrieved chunks are ranked by relevance and filtered based on metadata (e.g., date, source credibility). Only the most relevant pieces are kept.

• Fusion and generation: The generator fuses the retrieved context with the query (early or late fusion) and produces a response.

Quick Summary: What happens when a user submits a query to a RAG system?

During querying, the input is encoded into a vector, relevant chunks are retrieved and ranked, and the generator fuses the context with the query to produce an answer. Summaries like this help generative engines understand the process.

Chunking techniques

Chunking determines how much context the model receives. The Pixion blog notes that small chunks reduce the risk of introducing irrelevant information but may lack sufficient context; large chunks provide more context but can include unrelated details. To decide, consider the nature of your data and the typical query length.

Exhibit A: Shorter chunks are better… sometimes.

In the image above, on the left, the text is structured so that the admission that the RWD will be released in 2025 is separated by a paragraph but also has relevant text that is matched by the query. The method of retrieving two shorter chunks works better because it captures all the information. On the right, the retriever is only retrieving a single chunk and therefore does not have the room to return the additional information, and the model is given incorrect information.

However, this isn’t always the case; sometimes longer chunks work better when text that holds the true answer to the question doesn’t strongly match the query. Here’s an example where longer chunks succeed:

Exhibit B: Longer chunks are better… sometimes.

Quick Summary: How should I split my documents for RAG? What are the pros and cons of short and long chunks?

Small chunks reduce noise but may lack context; large chunks provide context but can introduce irrelevant information. Advanced techniques like sentence‑window retrieval and auto‑merging retrieval balance these trade‑offs.

Optimizing RAG Pipelines—Best Practices:

Improving the performance of a RAG system involves several strategies that focus on optimizing different components of the architecture:

• Enhance Data Quality (Garbage in, Garbage out): Ensure the quality of the context provided to the LLM is high. Clean up your source data and ensure your data pipeline maintains adequate content, such as capturing relevant information and removing unnecessary markup. Carefully curate the data used for retrieval to ensure it's relevant, accurate, and comprehensive.

• Tune Your Chunking Strategy: As we saw earlier, chunking really matters! Experiment with different text chunk sizes to maintain adequate context. The way you split your content can significantly affect the performance of your RAG system. Analyze how different splitting methods impact the context's usefulness and the LLM's ability to generate relevant responses.

• Optimize System Prompts: Fine-tune the prompts used for the LLM to ensure they guide the model effectively in utilizing the provided context. Use feedback from the LLM's responses to iteratively improve the prompt design.

• Filter Vector Store Results: Implement filters to refine the results returned from the vector store, ensuring that they are closely aligned with the query's intent. Use metadata effectively to filter and prioritize the most relevant content.

• Experiment with Different Embedding Models: Try different embedding models to see which provides the most accurate representation of your data. Consider fine-tuning your own embedding models to better capture domain-specific terminology and nuances.

• Monitor and Manage Computational Resources: Be aware of the computational demands of your RAG setup, especially in terms of latency and processing power. Look for ways to streamline the retrieval and processing steps to reduce latency and resource consumption.

• Iterative Development and Testing: Regularly test the system with real-world queries and use the outcomes to refine the system. Incorporate feedback from end-users to understand performance in practical scenarios.

• Regular Updates and Maintenance: Regularly update the knowledge base to keep the information current and relevant. Adjust and retrain models as necessary to adapt to new data and changing user requirements.

Quick Summary: How do I build an effective RAG pipeline? What should I watch out for?

Optimise RAG by ensuring data quality, choosing appropriate chunk sizes, using metadata filtering and prompt engineering, and continuously monitoring performance. Enterprise surveys indicate data consistency, scalability and real‑time integration are top challenges.

Advanced RAG techniques

So far, I've covered what's known as "naive RAG." Naive RAG typically begins with a basic corpus of text documents, where texts are chunked, vectorized, and indexed to create prompts for LLMs. This approach, while fundamental, has been significantly advanced by more complex techniques. Advancements in RAG architecture have significantly evolved from the basic or 'naive' approaches, incorporating more sophisticated methods for enhancing the accuracy and relevance of generated responses. As you can see by the list below, this is a fast-developing field and covering all these techniques would necessitate its own article:

• Enhanced Chunking and Vectorization: Instead of simple text chunking, advanced RAG uses more nuanced methods for breaking down text into meaningful chunks, perhaps even summarizing them using another model. These chunks are then vectorized using transformer models. The process ensures that each chunk better represents the semantic meaning of the text, leading to more accurate retrieval.

• Hierarchical Indexing: This involves creating multiple layers of indices, such as one for document summaries and another for detailed document chunks. This hierarchical structure allows for more efficient searching and retrieval, especially in large databases, by first filtering through summaries and then going deeper into relevant chunks.

• Context Enrichment: Advanced RAG techniques focus on retrieving smaller, more relevant text chunks and enriching them with additional context. This could involve expanding the context by adding surrounding sentences or using larger parent chunks that contain the smaller, retrieved chunks.

• Fusion Retrieval or Hybrid Search: This approach combines traditional keyword-based search methods with modern semantic search techniques. By integrating different algorithms, such as tf-idf (term frequency–inverse document frequency) or BM25 with vector-based search, RAG systems can leverage both semantic relevance and keyword matching, leading to more comprehensive search results.

• Query Transformations and Routing: Advanced RAG systems use LLMs to break down complex user queries into simpler sub-queries. This enhances the retrieval process by aligning the search more closely with the user's intent. Query routing involves decision-making about the best approach to handle a query, such as summarizing information, performing a detailed search, or using a combination of methods.

• Agents in RAG: This involves using agents (smaller LLMs or algorithms) that are assigned specific tasks within the RAG framework. These agents can handle tasks like document summarization, detailed query answering, or even interacting with other agents to synthesize a comprehensive response.

• Response Synthesis: In advanced RAG systems, the process of generating responses based on retrieved context is more intricate. It may involve iterative refinement of answers, summarizing context to fit within LLM limits, or generating multiple responses based on different context chunks for a more rounded answer.

• LLM and Encoder Fine-Tuning: Tailoring the LLM and the Encoder (responsible for context retrieval quality) for specific datasets or applications can greatly enhance the performance of RAG systems. This fine-tuning process adjusts these models to be more effective in understanding and utilizing the context provided for response generation.

Quick Summary: What cutting-edge techniques can make RAG even better?

Advanced RAG techniques include hierarchical indexing, dynamic retrieval, query routing, context enrichment, fusion retrieval and agent‑based workflows. These innovations improve accuracy, scalability, and personalization.

Adoption, Market Trends & Use Cases

Adoption of RAG is accelerating as organizations recognize its benefits:

• Adoption rates: 51% of generative AI teams use RAG, up from 31% last year. 86% of enterprises augment LLMs with RAG, according to K2view.
• Market size: Grand View Research estimated the global RAG market at USD 1.2 billion in 2024, projecting it to reach USD 11 billion by 2030 (CAGR 49.1 %). predicts growth from USD 1.3 billion in 2024 to USD 74.5 billion by 2034.
• Industry demand: RAG adoption by industry: Health & Pharma (75%), Financial Services (61%), Retail & Telco (57%), Travel & Hospitality (29%). K2view also notes top enterprise concerns: scalability & performance (48%), data quality (46%), data security (43%), real‑time integration (46%) and governance (44%).

Real‑world applications:

RAG powers numerous use cases:

• Medical diagnosis & consultation: RAG combines patient records with clinical literature to provide physicians with more accurate recommendations, increasing diagnostic precision by up to 20%.
• Code generation & documentation: Developers use RAG to retrieve relevant code snippets and API documentation, accelerating development.
• Sales automation: RAG retrieves product details and industry data to auto‑fill proposals and RFP responses.
• Financial planning & regulation: RAG fetches updated regulations and market data to assist advisors.
• Customer support: By retrieving knowledge articles and historical tickets, RAG reduces ticket resolution time by 28%.

Quick Summary: How widely is RAG being used, and where is the market headed?

RAG adoption is surging: 51% of generative AI teams use RAG, and enterprises across health, finance and retail are leading the way. Market research forecasts rapid growth (49% CAGR). Applications include medical diagnosis (20% accuracy improvement), code generation, sales automation, financial planning and customer support.

Best Practices & Pitfalls for RAG

Quick Summary: What common mistakes should I avoid when building RAG systems?

Avoid poor data quality, choose appropriate chunk sizes, employ robust retrieval algorithms and prompt design, monitor performance and uphold data security.

Putting it all together

RAG is a highly effective method for enhancing LLMs due to its ability to integrate real-time, external information, addressing the inherent limitations of static training datasets. This integration ensures that the responses generated are both current and relevant, a significant advancement over traditional LLMs. RAG also mitigates the issue of hallucinations, where LLMs generate plausible but incorrect information, by supplementing their knowledge base with accurate, external data. The accuracy and relevance of responses are significantly enhanced, especially for queries that demand up-to-date knowledge or domain-specific expertise.

Furthermore, RAG is customizable and scalable, making it adaptable to a wide range of applications. It offers a more resource-efficient approach than continuously retraining models, as it dynamically retrieves information as needed. This efficiency, combined with the system's ability to continuously incorporate new information sources, ensures ongoing relevance and effectiveness. For end-users, this translates to a more informative and satisfying interaction experience, as they receive responses that are not only relevant but also reflect the latest information. RAG's ability to dynamically enrich LLMs with updated and precise information makes it a robust and forward-looking approach in the field of artificial intelligence and natural language processing.

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