Robin Marillia

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:

When integrating internal knowledge into AI applications, three main approaches stand out:

1. Prompt Context  Put 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.