Most AI apps start off as a single LLM call, and need to be expanded from there.
But the responses are generic/ sometimes wrong. They may not understand your domain. You need to make it better. The question is: should you fine-tune your model or implement RAG?
This is a decision that will determine your project’s success, budget, and how many late nights you’ll spend debugging.
So, which one is better?
It’s not about better. It’s all about matching the technique to your specific problem.
This article will break down exactly when to use which approach across every major AI task category.
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Understanding the Fundamentals
We’ll look at some scenarios, but first let’s clarify what we’re dealing with.
Fine-tuning takes a pre-trained model and continues training it on your specific dataset. You’re literally updating the model’s weights and parameters to bake your knowledge directly into it.
RAG (Retrieval-Augmented Generation) keeps the base model unchanged but augments it with an external knowledge retrieval system. When a query comes in, the system first searches your knowledge base for relevant information, then feeds that context to the LLM along with the original query. The model generates responses based on both its training and the retrieved information.
The key difference is that fine-tuning embeds knowledge into the model itself. RAG looks up knowledge on-demand.
When to Use What
Most AI tutorials give you toy examples. But you’re building real systems. So let’s cover the full spectrum of scenarios you’ll actually encounter.
1. Question Answering Systems
Use RAG when:
Your knowledge base changes frequently (documentation, policies, regulations)
You need to cite sources and maintain transparency
Dealing with vast, diverse information that updates regularly
Building customer support chatbots that need current information
Example: A company knowledge base chatbot. Documents get updated weekly. New products launch monthly. With RAG, you simply add documents to your vector database and the system immediately has access. No retraining required.
If you’re building something like this, understanding how VectorDBs work internally will save you countless hours of optimization.
Use fine-tuning when:
Questions have predictable patterns with stable answers
You need extremely fast response times without retrieval overhead
Working with closed-domain Q&A where the knowledge rarely changes
The task requires memorisation of specific facts and relationships
Example: Medical exam question answering where questions follow established patterns and medical knowledge is relatively stable year-to-year.
2. Text Classification, Sentiment Analysis etc.
Use fine-tuning when:
You have labeled training data specific to your domain
Classification requires understanding nuanced domain-specific language
Need consistent, reliable categorisation without external lookups
Working with specialised terminology (legal, medical, financial)
Example: Financial sentiment analysis on earnings call transcripts. The language patterns are unique to finance—”beat estimates” is positive, “headwinds” is negative. Fine-tuning a model like BERT or GPT on labeled financial texts teaches it these domain-specific sentiment indicators.
Use RAG when:
Classification decisions require external context or recent information
Need to adjust classification rules without retraining
Working with evolving taxonomies or policies
Example: Content moderation where policy definitions change. RAG lets you update your policy documents in the knowledge base, and the classifier immediately adapts its decisions based on the new guidelines.
3. Summarisation
Use RAG when:
Summarising documents that reference external information
Need to incorporate related context from multiple sources
Summarising news or research that builds on prior knowledge
Example: Summarising research papers while incorporating related work and background context from a database of papers.
Use fine-tuning when:
Need consistent summarisation style and format
Working in specialised domains (legal briefs, medical reports)
Summaries must follow specific structural patterns
Have examples of ideal summaries in your domain
Example: Generating radiology report summaries that follow hospital-specific formatting and terminology standards.
4. Code Generation and Programming Tasks
More realistically, you’ll be using agents here and RAG will be involved on the existing codebase for sure. But let’s discuss how the LLM component itself should work.
Use fine-tuning when:
Generating code in specific frameworks or internal libraries
Need to follow company coding standards and patterns
Working with proprietary APIs or domain-specific languages
Have codebase examples demonstrating desired patterns
Example: GitHub Copilot is a prime example. It’s fine-tuned on millions of code repositories to understand programming patterns across languages.
Use RAG when:
Code generation requires looking up current API documentation
Need to reference multiple code examples or documentation sources
Working with frequently updated libraries or frameworks
Combining code snippets from different sources
Example: A coding assistant that searches your company’s internal codebase for similar functions, then generates new code following those patterns.
5. Content Generation (Marketing, Creative Writing)
Use fine-tuning when:
Need consistent brand voice and writing style
Generating content that follows specific format templates
Have large corpus of example content in your desired style
Content patterns are stable and well-defined
Example: Generating product descriptions in your company’s specific tone and format. Fine-tune on thousands of existing descriptions.
Use RAG when:
Content requires current facts or statistics
Need to incorporate information from multiple sources
Generating content about recent events or trends
Want flexibility to update content guidelines without retraining
Example: Writing blog posts about industry trends where the system needs to pull recent statistics, news, and research findings.
6. Conversational AI and Chatbots
Use a hybrid approach:
This is where combining both techniques shines
Fine-tune for:
Conversational style and personality
Common question patterns and responses
Domain-specific dialogue management
Use RAG for:
Retrieving specific factual information
Accessing current data (prices, availability, policies)
Looking up user-specific information (account details, history)
Example: A banking chatbot that’s fine-tuned on conversation patterns and tone, but uses RAG to retrieve account balances, transaction history, and current interest rates.
If you’re building a RAG system for this, I highly recommend understanding smarter chunking strategies to ensure your retrieval returns the most relevant context.
7. Document Analysis and Information Extraction
Use fine-tuning when:
Extracting structured information from standardised documents
Working with consistent document formats (invoices, contracts, forms)
Need to recognise patterns specific to document types
Example: Insurance claims processing where you’re extracting specific fields from standardised claim forms. Fine-tune on labeled examples.
Use RAG when:
Analysis requires cross-referencing multiple documents
Need to incorporate external knowledge or regulations
Documents reference information not contained within them
Example: Legal document analysis where contracts reference regulations, case law, and related agreements that need to be retrieved and considered.
8. Multi-modal Tasks (Image + Text, Audio + Text)
Use fine-tuning when:
Tasks require deep understanding of modality relationships
Working with specialised image/audio domains
Have labeled multi-modal training data
Example: Medical image captioning where the model must understand radiology images and generate appropriate descriptions.
Use RAG when:
Multi-modal tasks require external context or reference information
Need to retrieve related examples or documentation
Example: Product image search where you match user photos to your product database and provide relevant information.
The Decision Matrix: Quick Reference Guide
Here’s how to make the choice:
Choose Fine-tuning if:
You have quality labeled training data
Your domain knowledge is relatively stable
You need consistent output format/style
Latency matters (can’t have retrieval overhead)
Task requires learned pattern recognition
Budget allows for training infrastructure
Choose RAG if:
Knowledge changes frequently
Need to cite sources and maintain transparency
Working with large, diverse knowledge bases
Want to update knowledge without retraining
Limited labeled training data
Need explainability (can see what was retrieved)
Consider Hybrid if:
Need both consistent behaviour AND current information
Have stable patterns but dynamic facts
Want specialised performance with factual accuracy
Building complex applications (chatbots, assistants)
Real-World Hybrid Examples
The most powerful systems combine both approaches:
Medical Diagnosis Assistant:
Fine-tuned on medical conversation patterns and diagnostic reasoning
RAG retrieves current research papers, treatment guidelines, and drug information
Legal Research Tool:
Fine-tuned for legal reasoning and document analysis
RAG retrieves relevant case law, statutes, and precedents
Customer Service AI:
Fine-tuned for conversational style and common issue resolution
RAG accesses current policies, product information, and customer history
Cost and Resource Considerations
Fine-tuning costs:
High upfront: GPU infrastructure, training time, data labeling
Lower ongoing: Standard inference costs once trained
Retraining: Expensive whenever knowledge needs updating
RAG costs:
Lower upfront: No model training required
Higher ongoing: Vector database storage, retrieval compute, increased latency
Updates: Cheap, just add documents to your knowledge base
My Hard-Earned Lessons
After implementing both approaches in production:
Start with RAG for most applications. It’s faster to build, easier to debug, and more flexible. You can always fine-tune later if needed.
Fine-tuning is not a silver bullet for accuracy. If your base model is hallucinating, fine-tuning might just teach it to hallucinate more confidently in your domain :)
RAG quality depends on your retrieval. A bad retrieval system gives the model garbage context. Taking RAG pipelines to 98% accuracy requires obsessive attention to retrieval quality.
Hybrid is harder than it looks. Combining both approaches introduces complexity. Make sure you actually need both before going there.
Your choice might change. I’ve started projects with fine-tuning only to realise RAG was better. And vice versa. Be willing to pivot.
Just Remember
There’s no universal “right” answer. The best approach depends on:
What you’re building
How your knowledge evolves
Your data availability
Your performance requirements
Your budget and timeline
But here’s my rule of thumb: If you’re unsure, start with RAG. It’s faster to implement, easier to iterate on, and more forgiving of mistakes. You can always add fine-tuning later if you need that extra performance boost.
The goal isn’t to choose the “best” technique, it’s to choose the right technique for your specific problem. Now you have the framework to make that decision :)
P.S. Have I missed any use cases? Hit reply and let me know.