Production Systems

Problem — Teams needed accurate, cited answers from internal docs without hallucination.

Why it was hard — Balancing retrieval quality, context length, and latency for real-time chat.

Tech stack — LLM, BGE-M3, Qdrant, LangChain, FastAPI

Architecture — User query → embedding → vector search (Qdrant) → top-k retrieval → LLM prompt with context → streamed response.

Problem — Visitors wanted to ask questions about my work and get accurate, contextual answers.

Why it was hard — Building a small, reliable RAG pipeline with minimal infra and clear observability.

Tech stack — Next.js, Django REST, OpenAI / Llama, Qdrant, PostgreSQL

Architecture — Next.js frontend → Django API → embedding + vector search → LLM → response. Optional caching and rate limiting.

Problem — Large volumes of documents needed consistent tagging and routing for downstream workflows.

Why it was hard — Throughput, cost control, and handling edge cases without manual review.

Tech stack — Python, Transformers, Celery, PostgreSQL, S3

Architecture — Ingest → queue (Celery) → embedding + classifier → write labels and metadata → trigger workflows.