Advanced RAG Pipelines and Evaluation

Advanced RAG pipeline optimization framework using DSPy. Implements modular RAG pipelines with Query-Rewriting, Sub-Query Decomposition, and Hybrid Search via Weaviate. Automates prompt tuning and few-shot selection using MIPRO, COPRO, and BootstrapFewShot optimizers on datasets like FreshQA, HotpotQA, TriviaQA, Wikipedia and PubMedQA.

🚀 Production-ready modular RAG monorepo: Local LLM inference (vLLM) • Hybrid retrieval with Qdrant • Semantic caching • Docling document parsing • Cross-encoder reranking • DeepEval evaluation • Full observability with Langfuse • Open WebUI chat interface • OpenAI-compatible API • Fully Dockerized

A hands-on exploration of Deepeval — an open-source framework for evaluating and red-teaming large language models (LLMs). This repository documents my journey of testing, benchmarking, and improving LLM reliability using custom prompts, metrics, and pipelines.

🧠 LLM Validation Platform: Advanced testing frameworks with DeepEval, Playwright MCP, OpenLLMetry observability, Neo4j knowledge graphs, and Burr+pytest TDD workflows

Project demonstrating LLM testing using Deepeval with OpenAI and local LLMs as judge

A robust, modular pipeline for automated LLM chatbot evaluation, using DeepEval, GROQ models, and Confident AI dashboard logging. Designed for systematic QA, reliable evaluation, and portfolio-quality results in AI/QA engineering.

Framework for evaluating and improving LLM-generated scientific abstracts using ROUGE metrics, semantic embeddings, and LLM-as-judge techniques.

Sandbox Q&A bot for technical docs with optional DeepEval, LangChain, RAGAS, and OpenAI Evals integrations to compare RAG evaluation workflows.