Knowledge Management & RAG Systems (Search + GPT)
Recently, I started exploring RAG Systems (Retrieval Augmented Generation) and have been writing about my learnings. While the theory behind RAG excites us and we think to get started quickly, its real-world implementation presents an entirely unique set of challenges.
Previous blog post references:
It is important to realize that “Generative AI” responses are 80% unbiased and thoughtfully curated content and 20% AI generation. If the foundational content isn’t well curated or you don’t choose governed sources, Gen AI’s contribution will be garbage :).
The synchronization challenge
Experiencing the architecture of Keyword Search, Semantic Rerank, and Generative AI models like GPT-4 requires a harmonized knowledge base. Discrepancies in stop words, stemming techniques, synonyms, and organizational acronyms can lead to inconsistent results. For example: While you search understand the synonym or an acronym of your organization and if the re-ranking algorithm or even GPT-4 doesn’t have the knowledge then the user may receive mismatched responses. It is critical for the knowledge around domain synonyms and acronyms to be synchronized across the architecture.
Knowledge isn’t static
Knowledge changes, if you are working on last year's dataset, we expect some hallucinations or irrelevant outputs, keep your data fresh or the RAG system might serve wrong insights, it is also important to scope the RAG generation to only governed and curated data. Having a good knowledge management strategy and empowering the content curators is key.
The Role of Context
While user context & accurate search results passed as a request to GPT are key to delivering relevant content, It is also important to have knowledge context so that these algorithms can generate human-like responses by considering the context in which words or actions occur ex: “onboarding” may mean differently based on the context associated to what kind of onboarding ex: is it cloud onboarding, employee onboarding, etc.
The breadth vs Depth of knowledge
While it is super tempting to just load the entire knowledge into RAG system, it is crucial to balance, too much information can overwhelm the system, leading to diluted outputs. knowledge management practices enable curating a balanced dataset that offers both breadth and depth based on the user need and feedback loop.
Feedback loops and Continuous learning
Incorporating Humans in the loop, understanding feedback, evolving knowledge via self-service, and empowerment of curators, while also enabling users to flag inaccurate responses can be invaluable. This not only refines the system but also enriches the knowledge base, ensuring it remains updated and putting the user at the center.
Ethics considerations
While the Gen AI models are now strong in impersonating voice, tone, and personality and behave based on the boundaries, if the content has biases then it may impact the answers, Regular review of knowledge for biases, inclusion, and ensuring a good feedback loop is critical for the success of RAG system.
In summary, RAG systems are amazing “tech” but they are as good as your knowledge management strategy, approach, and empowered domain content experts. The real MVP (most valuable player) is “Effective Knowledge Management”.
I will keep writing more as I learn! Stay tuned.
