This is among the most common questions we get when building our AI agent framework. Our clients are always asking: "How do I train my agent on my own data?" RAG or fine-tuning?
Both are implemented in our framework, and I have seen quite a lot of different use cases over the years. I found it helpful to pass on my experience to new learners who have difficulties figuring out what is best for their case.
That's why I'm writing this article. To pass on what I've learned and hopefully help you make the right decision.
Two Ways, One Big Decision
One of the biggest decisions you'll make in creating agents is this: How do I enhance this agent in my own space? GPT-4 is amazing, but how would it ever know about your company's internal operations or special lexicon?
Two general approaches I see in our model:
- RAG: "Let the agent be on its own, I'll give it the information it needs at runtime"
- Fine-tuning: "I'll train the agent with my data"
Which one makes more sense? Having been a maintainer of a framework for years: it depends on your specific needs.
Start with RAG for your first project. It's faster to implement, cheaper to test, and you can always add fine-tuning later if needed.
RAG: "Real-time Information Feeding" Approach
How Does RAG Work?
Think about RAG from an agent framework perspective: When the agent is assigned a task, it first queries its knowledge base, gets associated context, and then goes ahead to the LLM with this context.
Here's how we accomplished that in our framework:
- Document ingestion pipeline
- Vector embedding and indexing
- Semantic search at agent runtime
- Context injection before LLM invocation
That's easiest for our users. Plug-and-play code.
RAG's Super Powers
Latest information: One of the highlights of our system. Client uploads a fresh document, agent starts utilizing that information instantly. Real-time information update.
Cost-effective: Agent deployment does not change. Knowledge base alone is updated. Infrastructure cost is minimal.
Traceability: When you debug, when you wonder "Why did the agent make this decision?", you are able to view which documents it was being fed from. This is particularly useful in framework development.
Multi-domain flexibility: Single agent, multiple knowledge bases per client. Wonderful for scalability.
RAG's Advanced Capabilities
RAG has evolved beyond simple text retrieval. Modern implementations now support multimodal RAG, which can process images, documents, and other media types together. This opens up entirely new possibilities for agent interactions.
RAG's Challenges
Problems I've seen while developing the framework:
Retrieval quality dependency: If you implement the system poorly, the agent is fed useless information. Garbage in, garbage out.
Context window management: You have to inject 4-5 heterogeneous document pieces but token limit says "no". Then you need priority logic.
Latency overhead: Vector search on every agent action. Might be problematic in performance-critical applications.
RAG adds latency to every query. If you're building high-frequency trading bots or real-time systems where milliseconds matter, carefully measure this overhead before committing to RAG.
Compare Model Responses
RAG Response
Retrieved Chunks:
Fine-tuned Response
Performance Insights
• Latency Difference: 1.5s faster with fine-tuning
• Token Usage: 23% fewer tokens with fine-tuning
• RAG provides source context but requires additional processing time
• Fine-tuned model gives direct answers but needs retraining for new information
Fine-tuning: "Customized Agent" Approach
What Does Fine-tuning Mean in Agent Framework?
You use the base model and fine-tune it for the client's specific application. The agent is converted into a native speaker of that domain.
We support these strategies in our framework:
- LoRA/QLoRA: Parameter-light, easy to deploy
- Task-specific fine-tuning: Fine-tuning for specific agent actions
- Domain adaptation: Industry vocabulary and behavioral patterns
Benefits of Fine-tuning
Consistency: Agent behaviors are predictable. This is very important for frameworks - user experience becomes consistent.
Performance: No retrieval overhead. Agent returns optimized response directly.
Specialized capabilities: Code generation, specific writing styles, domain expertise. fine-tuned agents are really good in these niches.
Offline operation: No network dependency. Required for edge deployments.
Fine-tuning Tools and Frameworks
For those interested in getting hands-on with fine-tuning, tools like LLaMA Factory have made the process much more accessible. They provide unified interfaces for fine-tuning various models with different techniques.
Fine-tuning's Challenges
Some of the issues I have faced with fine-tuning:
Training infrastructure: GPU clusters, distributed training setup. operations complexity increases.
Version management: Model versioning by client. Storage and deployment can get complicated.
Data requirements: It's challenging to obtain quality training data. Clients may not have enough quality data.
Static knowledge: Adding new information post-training means that you will have to retrain. Iteration cycle becomes longer.
Overfitting Risk: Fine-tuning with small datasets can make your model forget general knowledge. Always use validation sets and monitor performance on general tasks, not just your specific domain.
From Framework View: Which One to Employ?
Find Your Ideal Approach
How often does your data change?
What's your response time requirement?
What's your data volume?
Best Applications for RAG
Knowledge-intensive agents: Research assistants, documentation bots, customer support. They are our most trendy use cases in our framework.
Rapid prototyping: If you need quick setup for POC. You can get a demo up and running in 1-2 days by using RAG.
Dynamic content: Real-time data processing agents, news aggregation. RAG excels here.
Best Applications for Fine-tuning
Style-specific agents: Code generation, creative writing, idiosyncratic communication styles. We do a lot of this for our enterprise customers in our system.
Performance-critical applications: Low-latency, high-throughput needs. Financial trading bots, real-time decision-makers.
Compliance-sensitive domains: Healthcare, law, finance. Places where agent behavior needs to be completely predictable.
For compliance-sensitive domains (healthcare, finance, legal), fine-tuning often provides the predictable behavior patterns required for regulatory approval. RAG's dynamic retrieval can be harder to audit.
RAG Success Stories
One of our web shopping customers uploaded their full product list and customer policy into the RAG system. The agent is now answering "What is the return period of this product?" questions instantly.
A second client - a consulting firm - uploaded all their project case studies into the system. Their sales people can now instantly look up relevant cases during prospect meetings.
Toning Up Success Stories
One of our fintech clients optimized their risk assessment agent. It is now aware of company-specific risk parameters and gives consistent scores.
Our software development client optimized their code review agent. It is now familiar with company coding standards and finds style guide violations.
Hybrid Approach: Our Framework's Secret Sauce
Mixing both is sometimes the best method.
RAG + Fine-tuned combo: Fine-tune the agent to domain, and then feed it real-time knowledge with RAG. Best of both worlds.
Progressive enhancement: Start with RAG, collect user feedback, and then fine-tune for high-stakes applications.
We developed custom tooling for these hybrid approaches in our framework.
Hybrid approaches require more complex infrastructure but can deliver the best results. Consider this path when you have both: the budget for fine-tuning AND the need for dynamic knowledge updates.
Practical Implementation Advice
Implementing RAG
We recommend this stack in our framework:
- Embedding:
sentence-transformers/all-MiniLM-L6-v2is ideal to start - Vector DB: FAISS for dev, Pinecone/Weaviate for prod
- Chunking strategy: 50 token overlap generally a good default for 512 token chunks
If you want to see RAG in action, check out our guide on building RAG chatbots which walks through a complete implementation.
Document Quality Matters: Spend time cleaning and structuring your documents before ingesting them into RAG. Poor document quality leads to poor retrieval results, no matter how good your embedding model is.
Fine-tuning Setup
- LoRA configuration: r=16, alpha=32 as initial point
- Data quality: minimum 500+ high-quality samples
- Validation: hold-out test set to use, beware of overfitting
Data Quality Over Quantity: 100 perfectly crafted examples often outperform 1000 mediocre ones. Invest in high-quality, diverse training data rather than just collecting large amounts.
Framework Roadmap: What's Next?
As an Automatic RAG-to-Fine-tuning pipeline: Assess RAG usage patterns and offer automatic fine-tuning suggestions.
Smart retrieval optimization: ML models that adapt retrieval strategy based on agent behavior.
One-click hybrid deployment: A deployment pipeline that seamlessly bridges RAG and fine-tuning.
My Recommendations
Patterns I've observed as a framework maintainer:
RAG for MVP: Rapid, cheap, easy to iterate.
Fine-tuning for production optimization: When performance and stability are critical.
Hybrid for enterprise: When you need flexibility and performance.
My suggestion? Experiment with both approaches with your specific use case. We have test cases in our framework but every domain is different.
What approach does it appear to be most suitable for your project? What features in our framework would suit you best?
I wrote this entry to share lessons learned from building and maintaining our framework. Hope you find it helpful in deciding on the right approach to your agent use case.