Wed. Jun 12th, 2024

Let’s keep this precise and visual as possible!

Problem Architecture:

Below is a typical ChatGPT experience, great for general knowledge, but useless on your own data and context;

With Retrieval Augmentation Generation we add in additional steps to essentially replace the general knowledge of a LLM with contextual knowledge:

Pipeline Architecture:

RAG is split into two parts: Index Phase, where we transform our data into a machine readable format which maintains semantic relationships via embeddings:

The second phase is the query phase where we transform a question asked via an end user into a contextual response based on our data only:

Simple’s right?

Not so fast! How do you;

– Repeat this process

– Continuously improve this process

– Cater for advance RAG scenarios, like large data sources, like hierarchical data (it is not flat!) and complex relationships

There isn’t a silver bullet answer to this approach, but the emerging practice of RagOps is here to ellievate these constraints. 

By Harrison Kirby

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