Retrieve augment and generate (RAG): A technique to hack LLMs to your command
What is RAG, and Why is it Important?
Retrieve-Augmented Generation (RAG) is a powerful technique for making Large Language Models (LLMs) more effective, accurate, and context-aware by enhancing their responses with external knowledge sources. It combines retrieval (finding relevant text from documents) with generation (LLM text creation), effectively enabling models to respond with updated, accurate information not contained in their original training data. To understand the relevance of RAG, let’s first briefly touch upon how LLMs work and the limitations they possess.
Understanding LLMs and Their Limitations
Large Language Models like GPT-4 or Llama 3.1 are trained on enormous datasets containing trillions of tokens. For example, Llama 3.1 was trained on roughly 15 trillion tokens. To visualize this scale, imagine each token as part of an average-sized book: 15 trillion tokens would fill about 112 million books, each 400 pages long. To put that into perspective, if a person read nonstop, 24 hours per day without breaks, it would still take approximately 145,000 years to finish reading through this entire collection just once!
Despite this vast training, LLMs operate using a relatively straightforward process — predicting the next word or token given a prompt. Due to this method, they’re sometimes labeled “stochastic parrots,” as they parrot facts and linguistic patterns learned from massive data sets rather than truly understanding context.
The architectural design of LLMs introduces three significant weaknesses:
- Knowledge Cutoff: LLMs have a fixed knowledge cutoff date. Information published after this date is beyond their capabilities.
- Hallucinations: Models may confidently provide incorrect answers, known as hallucinations, because they’re simply predicting plausible continuations without reliable verification.
- Context Window Limit: Every LLM has a maximum token limit for inputs. Longer inputs cause earlier information to be lost or truncated, affecting response quality.
Why Not Just Include All Your Documents in the Prompt?
You might wonder: why not just include all relevant information directly into every prompt? There are several reasons this approach fails:
- Context Window Limitations: LLMs have fixed token limits, making it impossible to input extensive documentation simultaneously.
- Needle-in-a-Haystack Test: As the prompt length increases, it becomes more difficult for the model to accurately retrieve specific facts.
- Cost and Efficiency: Continuously sending large amounts of text to the model for every query is costly and slows response times significantly.
- Relevance: Typically, only a small portion of your documents is relevant for any given query.
How Does RAG Solve These Problems?
RAG addresses these challenges by augmenting the LLM’s generation capabilities with external knowledge retrieval, delivering precise, contextually accurate answers in a cost-effective manner. Rather than training or fine-tuning the entire model for every specific use case, RAG fetches only the necessary information at query-time.
Popular implementations include chatbots deployed on company websites and apps that provide accurate answers based on specific product documentation or private databases.
RAG vs fine-tuning:
RAG is a simple, cost-effective, practical and quick fix to make LLM answer questions from the context we provide it without hallucinations.
RAG Workflow: How it Works Step-by-Step:
Rag can be broken down into indexing or popularly known as creating vector stores for the knowledge document, retrieval and generation. A vector store is nothing but storing content or text of the knowledge documents in a format that helps the machine to understand the semantic relationship between words. So, for example, the words book and novel can be associated closely in the vector space.
Retrieval basically means, using the user query which is converted to vectors to extract relevant texts from the vector database /store which along with the user query is sent to the LLM to generate answers in conversation-like sentences.
Here’s the simplified RAG workflow:
1. Chunk Splitting
- Documents are broken down into manageable chunks (e.g., 500-token segments) to optimize retrieval efficiency.
- These chunks maintain semantic completeness and overlap slightly to preserve context continuity.
2. Embedding and Vector Store
- Each chunk is transformed into numerical representations called embeddings, which capture semantic meaning.
- Embeddings are stored in a vector database optimized for fast similarity searches.
3. Query and Retrieval
- User queries are embedded similarly to document chunks.
- The system retrieves the most relevant chunks from the vector store based on semantic similarity.
4. Augmenting the Prompt
- The retrieved chunks are combined with the user’s question to form an augmented prompt, providing contextually accurate input for the LLM.
5. Generating an Answer
- The LLM processes the augmented prompt to generate a response that’s grounded in accurate, context-specific knowledge, significantly reducing hallucinations.
Rag problems and strategies:
RAG might look invincible, but it also has some problems which lets it hallucinate from the context of knowledge documents caused by missing or misleading retrievals, latency issues cause problems to make rag scalable and crossing token limit issue for large documents.
Knowing the working of RAG, let’s see an overview of strategies. In the indexing or creating vector store we can choose the best strategy for our use case and also chunking strategies for getting vectors from knowledge doc which makes sure during retrieval memory is optimized, latency decreases and accuracy increases. In retrieval with similar end goals we can see whether the query needs a lot of semantic matching for retrieval, only keywords for retrieval etc. In generation, we can choose strategy based on whether we just take user queries and retrieved texts for generation, summarized text before generation, rewriting query before generation, fact verification during generation, standardized output for user queries where answer is unclear etc.
Current RAG implementations often struggle with:
- Latency due to large-scale retrieval.
- High compute costs for frequent LLM calls.
Future of rag:
RAG is evolving rapidly, and its future will be shaped by improvements in retrieval efficiency, multi-modal capabilities, agentic intelligence, real-time adaptability, and enhanced personalization. As AI models become more advanced, RAG will transition from being a passive retrieval mechanism to a highly interactive, intelligent, and context-aware system.
Key Trends Shaping the Future of RAG
1. Agentic RAG — Intelligent Decision-Making
Traditional RAG pipelines rely on simple retrieval and augmentation. However, agentic RAG introduces decision-making capabilities where the LLM acts as an agent that:
- Chooses the most relevant data sources dynamically.
- Determines the best format for presenting responses (text, charts, code, or tables).
- Integrates multiple knowledge bases, real-time data, and third-party services.
This evolution makes RAG more adaptive and autonomous, allowing AI systems to think beyond simple retrieval and deliver more meaningful responses.
2. Multi-Modal RAG — Beyond Text Retrieval
Future RAG systems will expand beyond text-based retrieval to include:
- Image & Video Retrieval — Extracting context from images, videos, and diagrams.
- Audio & Speech Processing — Using transcribed conversations for context-aware responses.
- 3D & Spatial Data — Applications in augmented reality (AR) and virtual reality (VR).This multi-modal approach will enhance applications in medical diagnosis, legal case analysis, technical documentation, and customer support.
3. Real-Time & Streaming Data Integration
Current RAG systems primarily rely on static databases. The future will see:
- Integration with live databases (e.g., financial markets, weather updates, sports scores).
- Streaming data retrieval (IoT sensors, real-time logs, dynamic knowledge graphs).
- On-the-fly knowledge updates (eliminating model hallucinations and outdated responses).
This will make RAG more relevant for business intelligence, emergency response, and finance applications.
4. Personalized & Contextualized RAG
RAG will become hyper-personalized, meaning:
- AI systems will remember user preferences and tailor responses accordingly.
- Personalized learning assistants will adapt to individual learning styles.
- Enterprise applications will serve different roles (e.g., a CEO vs. an engineer gets different insights from the same query).
This level of personalization will boost AI adoption in industries like education, HR, and sales automation.
5. Federated & Privacy-Preserving RAG
As AI regulations tighten, future RAG systems will:
- Work with decentralized data sources without exposing sensitive information.
- Use federated learning to improve retrieval models while maintaining data privacy.
- Leverage encrypted and differential privacy techniques to prevent information leakage.
This is crucial for applications in healthcare, legal compliance, and financial services.
Future RAG systems will optimize retrieval using:
- Hierarchical indexing (faster lookup with minimal compute).
- Edge AI & On-Device Processing (reducing dependency on cloud-based LLMs).
- Cognitive AI models that learn which data is most valuable for retrieval, reducing unnecessary lookups.
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