You want to deploy a large language model that knows about your organization—your policies, your products, your internal documentation. The model needs to answer questions accurately using information that wasn't in its original training data.
You have two main options: Retrieval-Augmented Generation (RAG) or fine-tuning. Both work. Both have trade-offs. Choosing wrong can cost months of effort and significant budget. Here's how to decide.
Understanding the Approaches
Retrieval-Augmented Generation (RAG)
RAG combines a pre-trained language model with a retrieval system. When a user asks a question, the system first searches a knowledge base to find relevant documents, then passes those documents to the LLM along with the question. The model generates an answer grounded in the retrieved content.
Think of it as giving the model an open-book exam. The knowledge lives outside the model in searchable documents. The model's job is to read the provided context and synthesize an answer.
Fine-Tuning
Fine-tuning modifies the model's weights by training on domain-specific examples. You provide pairs of inputs and desired outputs, and the model learns to generate similar outputs for similar inputs. The knowledge gets encoded into the model itself.
Think of it as studying for a closed-book exam. The model internalizes the information during training and recalls it from memory during inference.
When RAG Is the Right Choice
RAG excels when:
Information changes frequently: If your knowledge base updates daily or weekly, RAG handles this naturally—you update the document index, and new information is immediately available. Fine-tuning would require expensive retraining cycles.
Accuracy and traceability matter: RAG can cite its sources. When the model answers based on retrieved documents, you can show users exactly where the information came from. This is essential for compliance, healthcare, legal, and financial applications where "trust but verify" is the norm.
You have lots of diverse content: RAG scales well with content volume. Adding thousands of documents to a vector database is straightforward. Fine-tuning on massive, diverse content is expensive and can cause the model to forget or confuse information.
You need to control what the model knows: RAG systems can be scoped to specific document collections. Different users can access different knowledge bases. You can exclude sensitive information without retraining.
Latency tolerance exists: RAG adds retrieval time to every request. For most applications, an extra 100-500ms is acceptable. For real-time systems with strict latency requirements, it might not be.
When Fine-Tuning Is the Right Choice
Fine-tuning excels when:
You need behavior change, not just knowledge: If you want the model to respond in a specific style, follow particular formatting conventions, or exhibit certain reasoning patterns, fine-tuning is often more effective than trying to enforce this through prompts.
Knowledge is stable: Core principles, historical facts, established procedures—information that doesn't change can be baked into model weights reliably.
Latency is critical: Fine-tuned models answer from memory without retrieval overhead. For high-frequency, latency-sensitive applications, this matters.
You're working with specialized domains: Models trained primarily on web text may not understand niche terminology, domain-specific concepts, or unusual data formats. Fine-tuning can teach this specialized understanding more effectively than RAG, which still relies on the base model's comprehension.
Context window limits are constraining: RAG consumes context window with retrieved documents. If your queries need the full context for other purposes, or if relevant information spans many documents, fine-tuning avoids the context crunch.
The Hybrid Approach
In practice, many production systems combine both techniques. Fine-tuning establishes domain understanding, communication style, and reasoning patterns. RAG provides access to current, detailed, citable information.
A customer support system might fine-tune a model to match company voice and understand product terminology, while using RAG to retrieve specific policy documents, troubleshooting guides, and recent announcements.
Implementation Considerations
RAG Requirements
Document processing pipeline: You need to chunk documents appropriately, generate embeddings, and maintain a vector database. Chunking strategy significantly impacts retrieval quality—too small and you lose context, too large and you retrieve irrelevant content.
Retrieval tuning: Off-the-shelf embedding models may not work well for your domain. You may need to experiment with retrieval strategies, reranking models, and hybrid search approaches.
Prompt engineering: How you present retrieved context to the model matters. Instructions about prioritizing retrieved information over training knowledge, handling contradictions, and admitting uncertainty require careful design.
Fine-Tuning Requirements
Training data: You need examples—typically hundreds to thousands of high-quality input-output pairs that demonstrate desired behavior. Creating this data is often the hardest part.
Compute resources: Fine-tuning large models requires significant GPU resources. Managed fine-tuning services simplify this but at a cost.
Evaluation frameworks: How do you know if fine-tuning helped? You need robust evaluation sets and metrics to measure improvement and catch regressions.
Version management: Fine-tuned models are artifacts that need versioning, testing, and deployment pipelines—just like any software.
Decision Framework
Start with these questions:
How often does the knowledge change? Frequent updates favor RAG.
Is traceability required? Citation needs favor RAG.
Are you changing behavior or adding knowledge? Behavior changes favor fine-tuning.
What are your latency requirements? Sub-100ms needs favor fine-tuning.
How much training data do you have? Limited examples favor RAG.
What's your operational capacity? RAG requires retrieval infrastructure; fine-tuning requires training pipelines. Pick based on your team's strengths.
The Bottom Line
There's no universally correct answer. RAG is often the right starting point—it's faster to implement, easier to iterate, and more forgiving of mistakes. But it's not always sufficient.
The most robust production systems use both approaches thoughtfully, leveraging each technique's strengths. Start with the simplest solution that might work, measure its shortcomings, and add complexity only where necessary.