RAG vs CAG vs Fine-Tuning

Three proven techniques to close the knowledge gap in large language models

Jun 29, 2025

Closing the Knowledge Gap in LLMs: RAG vs CAG vs Fine-Tuning

You ask your AI assistant a question. It confidently returns an outdated answer. The problem? The model was never given access to the latest information.

Large language models are powerful, but they’re not omniscient. They operate within a knowledge boundary: what was baked into their training data, what they’re prompted with, and what they can access in real time. When the answer falls outside that boundary, you hit what I call a knowledge gap.

The good news? You’ve got tools. Three of them: RAG, CAG, and fine-tuning. But they’re not interchangeable. Choosing the wrong one can cost you speed, money, or worse, trust.

Let’s break them down, compare them side by side, and explore real-world use cases to see where each one shines.

RAG: Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) works by dynamically retrieving relevant documents at inference time and injecting them into the model’s context.

Unlike basic keyword search, which looks for exact matches, RAG uses vector-based retrieval: it compares the semantic meaning of your question to the meaning of chunks in your knowledge base. Even if the phrasing is different, it can still find relevant content.

Once the most relevant chunks are retrieved, they’re combined with the user query and passed as a single prompt to the LLM. The model then generates a grounded response using both the retrieved content and the original question.

It’s a blend of semantic search and language generation: find the right chunks, let the model explain them.

Retrieval-Augmented Generation (RAG) Pipeline — How Retrieval-Augmented Generation (RAG) Works

Here’s how the process typically works:

Ingestion: Knowledge sources are loaded and split into smaller chunks using heuristics like headings, sentences, or token limits. This step is done ahead of time and ends once the chunks are indexed. The quality of chunking directly impacts retrieval accuracy later on.
Embedding: Each chunk is passed through an embedding model, which transforms the text into dense vector representations (embeddings) that capture semantic meaning. Popular embedding models include OpenAI’s text-embedding-3-small and open source options like all-MiniLM-L6-v2.
Indexing: These vectors are stored in a vector database such as Weaviate, Pinecone, Chroma, or PostgreSQL with pgvector. This setup enables fast similarity searches using algorithms like cosine similarity or dot product to efficiently find the most relevant chunks.
Querying: When a user asks a question, it is embedded using the same embedding model and compared against the stored vectors to find the most semantically similar chunks. This step happens in real time and determines which pieces of context will be passed to the model for answering.
Prompting: The top-ranked chunks are inserted into the LLM’s prompt, enriching the user’s original query with factual, retrieved context. This helps both proprietary models (like ChatGPT, Claude, or Gemini) and open-source ones (like Mistral or LLaMA) generate responses that are more accurate, specific, and grounded in the source material.

This allows the model to answer questions using external knowledge, even if that information wasn’t included in its original training data.

Strengths:

Well-suited for large, frequently changing knowledge bases
No model retraining required; update your documents and reindex
Fast to deploy and relatively easy to maintain with the right tooling

Weaknesses:

Retrieval failures are silent; irrelevant chunks can slip in undetected
Adds latency from embedding, vector search, and prompt assembly
Heavily dependent on chunking quality and search accuracy

Think of RAG as the model using semantic Google. Ask it about “revenue growth,” and it might retrieve chunks discussing “quarterly performance” or “sales trends.” Different words, same idea.

CAG: Cache-Augmented Generation

Cache-Augmented Generation (CAG) preloads a static knowledge base into the model’s internal attention cache (KV cache) and reuses that memory during inference. Instead of embedding and retrieving documents at runtime like RAG, CAG processes and stores them ahead of time. The result is faster, retrieval-free responses.

The approach was introduced in the paper “Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks” as a simpler and faster alternative for use cases where the knowledge is small, stable, and essential. The model doesn’t need to search. It already remembers.

Unlike prompt caching or external memory logs that replay past inputs or outputs, CAG works entirely inside the model. It uses the model’s own attention mechanism to preload documents into memory as key-value pairs. No external systems involved. No retrieval step required.

Cache-Augmented Generation (CAG) Pipeline — How Cache-Augmented Generation (CAG) Works

Here’s how it works:

Preloading: A selected set of documents is preprocessed and encoded to fit the model’s context window. These are transformed into attention-level key-value pairs that capture how the model internally interprets them. This cache is saved for reuse.
Inference: When the user submits a query, the model reuses the cached knowledge along with the prompt. Because the context is already in memory, it can respond instantly. No need to retrieve, rank, or re-embed.
Resetting: As the session evolves, new tokens are added. The model can discard only those recent additions while keeping the original cached knowledge intact, maintaining speed without reprocessing everything.

Strengths:

Latency is minimal, since the model skips retrieval entirely.
Responses are highly reliable, with no risk of selecting the wrong chunk.
Infrastructure is simpler, as there’s no need for vector databases or embedding pipelines.
Best suited for small, trusted knowledge sets that fit comfortably within the context window.

Weaknesses:

Context window limitations restrict how much knowledge can be preloaded.
Updates are manual and infrequent, making it less effective for dynamic domains.
Only works with models that support long context windows and cache control.

CAG is like photographic memory for models. It sees your documents once, remembers how it interpreted them, and reuses that understanding instantly, without having to look things up again.

Fine-Tuning: Rewriting the Brain

Fine-tuning takes a pretrained language model and continues its training on a specialized dataset. Instead of retrieving knowledge or preloading it, you embed the information directly into the model’s weights. The result is that the model doesn’t just remember your domain—it thinks like it. This changes how it interprets questions, makes decisions, and even writes.

Using supervised learning, you feed the model high-quality examples with both inputs and expected outputs. This allows it to learn domain-specific patterns, terminology, tone, and reasoning. Unlike RAG or CAG, fine-tuning doesn’t depend on the context window or runtime augmentation. Once trained, the knowledge lives inside the model and is always available.

This approach is especially useful when you need deep subject-matter expertise or tight alignment to brand voice, regulatory tone, or unique logic. However, it requires a significant upfront investment in data curation, compute resources, and evaluation.

Fine-Tuning Pipeline — How Fine-Tuning Works

Here’s how it works:

Prepare training data: Collect high-quality examples that reflect the behavior you want. These should include questions and ideal answers, written in the desired tone, style, and domain language.
Update the model’s weights: Use supervised training to teach the model from this dataset. Instead of adding information at runtime, you embed it into the model itself by adjusting its internal parameters.
Deploy the fine-tuned model: After validation, the model runs independently. It can now respond to domain-specific queries with speed and consistency, without needing prompts or retrieval. The knowledge is built in.

Strengths:

Fine-tuning integrates knowledge deeply into the model’s behavior.
Allows the model to recognize and generalize from domain-specific language and workflows.
You can fully customize tone, output style, and reasoning patterns.
There are no runtime augmentation steps or limitations from context length.

Weaknesses:

Fine-tuning requires a large, high-quality dataset to be effective.
Training and updating the model can be expensive and time-consuming.
If not done carefully, it may cause the model to forget important general knowledge.
Once fine-tuned, changes are baked into the model and are difficult to audit, undo, or adapt quickly.

Fine-tuning is like brain surgery for your model. It’s deliberate, precise, and irreversible. It’s the right approach when the problem runs deep, but overkill for surface-level fixes.

Comparing RAG, CAG, and Fine-Tuning

Here’s a quick side-by-side comparison to help you choose the right technique based on speed, flexibility, and complexity.

This table isn’t just a checklist. It’s a reflection of priorities.

RAG gives you flexibility.
CAG gives you velocity.
Fine-tuning gives you accuracy.

Choose the approach that best fits your knowledge needs and how much control you want over the model.

Real-World Use Cases

Let’s ground the theory in practice.

Below are five real-world problems and the techniques that solve them best. This way, you can see how RAG, CAG, and fine-tuning perform when it really matters: on the job.

1. Internal Helpdesk Chatbot

Problem: Employees need instant answers about time-off, expense reports, or device policies. The documents rarely change and fit in a small space.

Best fit: CAG
Preload the docs once, reuse them every time. Low latency, no external retrieval needed. RAG would be overkill. Fine-tuning is unnecessary unless you want the bot to mimic your CEO’s tone.

2. Customer Support Assistant

Problem: You're supporting multiple products with constantly changing documentation. Accuracy matters, and the knowledge base is large.

Best fit: RAG
Retrieves the freshest content at runtime. Updating is as simple as reindexing new docs. CAG would hit context limits. Fine-tuning would be a maintenance nightmare.

3. Specialized Legal Assistant

Problem: The model must understand contracts, statutes, and respond in legally precise language.

Best fit: Fine-tuning
You need the model to reason like a legal expert, not just repeat facts. CAG can't handle the scale. RAG might retrieve irrelevant precedent without deep context understanding.

4. SaaS Sales Assistant

Problem: The model needs to speak in your brand’s tone while pulling current product details, pricing, and offers.

Best fit: Fine-tuning + RAG
Fine-tune for tone and positioning. Use RAG to plug in up-to-date specs. CAG can't scale with constant product updates.

5. Clinical Decision Support

Problem: Doctors need accurate, comprehensive answers based on patient records, treatment guidelines, and drug interactions. They also tend to ask complex follow-up questions during consultations.

Best Fit: RAG + CAG
Use RAG to retrieve the most relevant documents from large medical databases. Then pass those into a long-context model using CAG to create a temporary working memory for each case. This hybrid approach combines breadth, accuracy, and responsiveness, making it well suited for real-time clinical decisions.

The best solutions often blend strengths from each. Choose what fits, and combine when needed.

Final Thoughts: Aligning Solutions with Needs

Large language models are impressive, but they don’t know everything. When answers fall outside their pretraining, you need a strategy to close the gap without breaking the system, or the budget.

RAG, CAG, and fine-tuning offer three distinct paths forward. RAG is versatile and scalable, ideal for fast-moving knowledge. CAG trades flexibility for speed, perfect for stable, high-trust domains. Fine-tuning rewires the model itself, offering precision and deep domain adaptation, at a higher cost.

There’s no universal winner. Your ideal approach depends on how much your knowledge moves, how fast you need answers, and how much control you want over the model’s voice and reasoning.

Need scale? Retrieve it.
Need speed? Cache it.
Need precision? Fine-tune it.

You don’t have to pick just one. The best solution is often a combination.

So next time your model says, “I don’t know,” don’t panic. You have options. Choose the one that fits.