GENERATIVE AI / 03
Better answers start with the right context.
Conversational applications that connect language models with relevant knowledge through retrieval-augmented generation.
The challenge.
A useful business assistant must retrieve relevant knowledge and connect it with the conversation, while fitting into an application people can actually use.
My contribution.
Designed and developed chatbot applications, retrieval workflows, Python APIs, and responsive conversational interfaces.
The approach.
Embedding-based similarity search retrieves context from knowledge sources. FastAPI services orchestrate retrieval and language-model requests, and React interfaces present the conversation.
The decisions behind it.
Retrieve context before generation
RAG workflows use FAISS or AWS OpenSearch to retrieve relevant information for context-aware responses.
Separate orchestration from the interface
Python services handle AI requests and retrieval while React and Next.js provide responsive application interfaces.
Build around application fundamentals
Application databases, OAuth-based access, Redis caching, and AWS integrations support the wider software experience.
What was delivered.
Developed conversational experiences and their supporting retrieval and application services. This is a summary of related engineering work, not a public product demo.
HAVE SOMETHING IN MIND?
Let’s make
something useful.
A thoughtful conversation is where good software begins.