The Evolution of RAG
* **From Simple RAG to Autonomous Agents: A Web Guide.**
Why RAG? The LLM's "Closed-Book Exam" Problem
LLMs are potent yet limited. RAG enhances them, turning "closed-book" models into "open-book" authorities through data access.
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Knowledge Cutoff
* Trained on a fixed dataset, LLMs are inherently unable to access real-time or new information.
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Fact Hallucination
* Uncertain LLMs sometimes output deceptive, albeit believable, data.
The Foundational RAG Blueprint
RAG systems operate in two main stages: offline data ingestion (preparation) and online query inference (processing).
Phase 1: Ingestion (Offline)
Loading
Source documents (PDFs, HTML, etc.) are loaded into the system.
Chunking
Large documents are broken into smaller, semantically meaningful pieces.
Embedding
Each piece transforms into a numerical vector of its significance.
Indexing
* **A vector database enables quick lookup of stored embeddings.**
Phase 2: Inference (Online)
Retrieval
* The query is embedded to identify the most suitable chunks.
Augmentation
* Chunks are joined with the query to build an augmented prompt.
Generation
* Using the augmented prompt, the LLM produces a grounded answer.
The Ladder of Complexity
RAG adapts; complexity grows. Simple search evolves to autonomous reasoning as needs become more intricate.
* This chart maps RAG implementation complexity, charting a path from simple baselines to sophisticated systems.
A Closer Look at Key Patterns
* Every RAG pattern is engineered to solve a distinct problem in the pipeline.
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Naive RAG
* Searches all files at once, ideal for simple questions but can cause "context pollution."
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Metadata Filtering
* Filtering by criteria (date, source) hones document searches, increasing precision.
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Re-ranking
* This improved model then re-orders findings, placing the most relevant at the top.
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Agentic RAG
Here are a few rewrites of the sentence, keeping the length and core meaning similar: * **An LLM agent independently chooses retrieval tools to address intricate questions.** * **For complex queries, an LLM agent self-selects from available retrieval toolsets.** * **To solve complex problems, an LLM agent uses its toolkit of retrieval methods.** * **A complex query triggers an LLM agent to autonomously use retrieval tools.**
Choosing Your RAG Architecture
* Tailoring requires trade-offs: consider complexity, cost, and the need for precision.
* The radar chart compares two RAG systems: 'FAQ Bot', optimized for speed and cost, and 'Research Agent', focused on accuracy and complexity, reflecting design trade-offs.