Your LLM sounds confident. It also makes things up. Fine-tuning on your data helps, but it's slow, expensive, and still produces models that confabulate with conviction.
Retrieval-Augmented Generation (RAG) takes a different approach: instead of hoping the model learned the right information, you retrieve relevant documents and hand them directly to the model at inference time. The model generates responses grounded in actual source material. That material you can verify, cite, and update without retraining.
Why it matters: Enterprise AI needs to work with your data. Policies, documentation, knowledge bases, product information. RAG bridges the gap between powerful language models and your proprietary information, enabling AI assistants that answer based on facts rather than statistical plausibility.
How RAG Works
RAG combines two systems: a retriever that finds relevant documents and a generator (the LLM) that produces responses using those documents as context.
The RAG Pipeline
- Query processing: User question is analyzed and potentially reformulated for better retrieval
- Document retrieval: Relevant documents or passages are fetched from a knowledge base using semantic search
- Context assembly: Retrieved documents are combined with the original query into a prompt
- Generation: The LLM produces a response grounded in the provided context
- Response delivery: Answer is returned, ideally with source attribution
Why Retrieval Matters
LLMs compress vast amounts of text into model weights during training. Specific facts get averaged, conflated, or lost. When asked about something not well-represented in training data, the model generates plausible-sounding content: hallucinations.
RAG addresses this by providing explicit context. Instead of relying on what the model learned (or didn't learn) about your product documentation, you retrieve the actual documentation and include it in the prompt. The model's job shifts from recall to synthesis.
RAG Architecture Decisions
Document Chunking
LLMs have context window limits. A 128K token window sounds large until you need to search across thousands of documents. Chunking breaks documents into retrievable pieces.
Chunking strategies:
- Fixed-size: Simple but may split mid-sentence or mid-concept
- Semantic: Chunk by paragraphs, sections, or topic boundaries
- Sliding window: Overlapping chunks to preserve context across boundaries
- Hierarchical: Summaries at document level, details at chunk level
The key challenge: chunks must be small enough for efficient retrieval but large enough to contain meaningful context. Include metadata or continuity markers so related chunks stay connected in embedding space.
Retrieval Methods
Sparse retrieval (BM25, TF-IDF): Keyword matching. Fast and interpretable but misses semantic similarity.
Dense retrieval (embeddings): Convert queries and documents to vectors, find nearest neighbors. Captures semantic meaning but requires embedding infrastructure.
Hybrid approaches: Combine sparse and dense retrieval, rerank results. Often the best of both worlds.
Multi-Query Retrieval
A single search may miss relevant documents. Strategies to improve recall:
- Query expansion: Generate multiple phrasings of the question
- Original + processed: Search with both the raw query and a reformulated version
- Decomposition: Break complex questions into sub-queries, retrieve for each
Different query formulations often retrieve different relevant documents. Combining results improves coverage at the cost of processing time.
Common RAG Challenges
Context Window Limits
Even large context windows fill up quickly when you need comprehensive coverage. Strategies:
- Aggressive ranking to surface the most relevant chunks
- Summarization of lower-ranked but potentially relevant documents
- Multi-turn retrieval that progressively narrows focus
Retrieval Quality
RAG is only as good as its retrieval. If relevant documents aren't retrieved, the model either hallucinates or declines to answer.
Improving retrieval:
- Domain-specific embedding models
- Metadata filtering to narrow search scope
- Relevance feedback loops using user behavior
- Evaluation of retrieval precision and recall
Coherence Across Chunks
When answers require synthesizing information from multiple chunks, the model may struggle to maintain coherence. This is especially true if chunks contradict each other or cover overlapping topics differently.
Solutions include careful chunk design with clear topic boundaries and explicit instructions for the model to reconcile conflicting information.
Ignoring Retrieved Context
Models sometimes ignore provided documents and answer from parametric memory anyway, especially for questions that feel "common knowledge." This defeats the purpose of RAG.
Mitigation: explicit prompting that instructs the model to base answers only on provided context, with techniques to verify faithfulness of responses to sources.
RAG vs. Fine-Tuning
| Aspect | RAG | Fine-Tuning | |--------|-----|-------------| | Update frequency | Real-time document updates | Requires retraining | | Source attribution | Natural: sources are explicit | Difficult: information baked into weights | | Knowledge scope | Unlimited document stores | Limited by training data size | | Setup complexity | Retrieval infrastructure required | Training infrastructure required | | Cost | Per-query retrieval costs | Upfront training costs | | Behavior modification | Limited to knowledge injection | Can modify reasoning, style, capabilities |
Often the answer is both: fine-tune for domain adaptation and reasoning patterns, use RAG for factual knowledge that requires currency and citation.
RAG Observability and Trust
RAG introduces new failure modes that require monitoring:
Retrieval Failures
- Relevant documents not retrieved
- Irrelevant documents surfaced
- Retrieval latency impacting user experience
Generation Failures
- Model ignores or misinterprets provided context
- Hallucinations despite accurate retrieval
- Inconsistent answers across similar queries
Pipeline Health
- Embedding drift as document corpus changes
- Index staleness from update delays
- Cost per query trending unexpectedly
AI observability for RAG systems tracks the full pipeline: what was retrieved, what was generated, whether the output faithfully reflects the sources, and where failures occur.
Building User Trust
RAG enables features that build user confidence:
Source citation: Show which documents informed the answer. Users can verify claims against original sources.
Streaming responses: Display answers progressively rather than after full generation. Creates more natural interaction.
Confidence indicators: Flag when retrieval quality is low or when the model expresses uncertainty.
Feedback mechanisms: Thumbs up/down and comment boxes capture user satisfaction, driving continuous improvement.
How Swept AI Supports RAG Systems
RAG doesn't eliminate AI risk. It changes where risk lives. Swept AI provides the trust layer for RAG deployments:
-
Evaluate: Test RAG pipeline quality before deployment. Measure retrieval precision, generation faithfulness, and end-to-end answer quality across your document corpus and user query patterns.
-
Supervise: Monitor production RAG systems in real time. Track which documents are retrieved, whether responses stay faithful to sources, and when hallucinations slip through despite grounding.
-
Distribution tracking: Understand how query patterns, document retrievals, and response characteristics shift over time. Detect when changes in your knowledge base or user behavior affect system quality.
RAG grounds LLMs in real data. AI supervision ensures that grounding is maintained, catching the cases where models ignore context, misinterpret sources, or generate plausible-sounding content that your documents don't support.
What is FAQs
RAG is an AI architecture that retrieves relevant documents from a knowledge base and provides them as context to an LLM, grounding its responses in actual data rather than parametric memory alone.
By providing source documents as context, RAG constrains the LLM to respond based on retrieved information rather than fabricating answers. This reduces but doesn't eliminate hallucinations. Models can still misinterpret or ignore provided context.
Fine-tuning modifies model weights with new training data. RAG keeps the base model unchanged and instead retrieves relevant information at inference time. RAG is faster to implement, easier to update, and provides source attribution.
Document chunking strategy, retrieval quality, context window limits, maintaining coherence across chunks, and balancing retrieval precision vs. recall.
RAG for knowledge that changes frequently, requires source citation, or comes from large document collections. Fine-tuning for adapting model behavior, style, or domain-specific reasoning patterns.