The differences between prompt context, RAG, and fine-tuning and why we chose prompting
When integrating internal knowledge into AI applications, three main approaches stand out:
1. Prompt Context – Load all relevant information into the context window and leverage prompt caching.
2. Retrieval-Augmented Generation (RAG) – Use text embeddings to fetch only the most relevant information for each query.
3. Fine-Tuning – Train a foundation model to better align with specific needs.
Each approach has its own strengths and trade-offs:
• Prompt Context is the simplest to implement, requires no additional infrastructure, and benefits from increasing context window sizes (now reaching hundreds of thousands of tokens). However, it can become expensive with large inputs and may suffer from context overflow.
• RAG reduces token usage by retrieving only relevant snippets, making it efficient for large knowledge bases. However, it requires maintaining an embedding database and tuning retrieval mechanisms.
• Fine-Tuning offers the best customization, improving response quality and efficiency. However, it demands significant resources, time, and ongoing model updates.
Why We Chose Prompt Context
For our current needs, prompt context was the most practical choice:
• It allows for a fast development cycle without additional infrastructure.
• Large context windows (100k+ tokens) are sufficient for our small knowledge base.
• Prompt caching helps reduce latency and cost.
What do you think is the better approach ? In our case as our knowledge base grows, we expect to adopt a hybrid approach, combining RAG for scalability and fine-tuning for more specialized responses.
I agree with your assessment and choice of prompt context as a starting point. For smaller knowledge bases, it offers the perfect balance of simplicity and effectiveness without overengineering.
As you scale, the hybrid approach makes good sense. RAG will help manage larger knowledge bases efficiently, while strategic fine-tuning can optimize for your most critical use cases. This gives you both breadth and depth.
One consideration: with RAG, invest time in your chunking strategy and embedding model selection early on. These foundational choices become harder to change later but significantly impact retrieval quality.
Have you explored any specific benchmarks to measure performance across these approaches for your particular domain?
@kevin_blondel great point about benchmarks ! We will definitely invest some time so measures latency and cost differences between techniques when migrating 👍
Your choice of prompt context makes sense for your current small knowledge base due to its simplicity and fast development. As your knowledge base grows, the hybrid approach of RAG and fine - tuning is a smart move. RAG will handle scalability well by efficiently retrieving relevant snippets, reducing token usage. Fine - tuning can then customize the model for specialized responses. It balances cost - efficiency, scalability, and customization. This combination should help you adapt to the expanding knowledge while maintaining performance and quality.
Interesting! Thanks for sharing @robin_marillia
Have you considered how you'll handle the transition phase when your knowledge base reaches the tipping point between prompt context efficiency and RAG necessity? That migration window often presents unexpected challenges.
If you're building a customer support AI with product documentation, you might face a scenario where some queries require deep context from multiple documents while others need only targeted information. Managing this mixed retrieval pattern during transition can be tricky - are you planning to implement parallel systems before fully switching over?
Thanks Robin, the real win was how our devs and product worked as one team on the Prompt Context implementation. We focused on making everything feel natural and snappy for users, while keeping things flexible for future updates.
Perfect example of what happens when UX and tech decisions go hand in hand! 🙌