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I will n8n rag engineer, drive document qa review qdrant dify agentic gpt5 lightrag ai
Sheldon Wisdom
About this gig
Your RAG should find the right evidence, not guess the answer.
I will build, audit, and optimize a RAG chatbot for accurate document Q&A, grounded in your own knowledge base, rulebook, policies, manuals, regulations, or technical documentation.
For new or existing RAG systems, I can work on document ingestion, semantic chunking, embeddings, vector search, hybrid search, reranking, retrieval, prompt design, source citations, answer grounding, and RAG evaluation.
I can implement Python RAG solutions with Claude AI and other LLMs, depending on your architecture and requirements.
Whether you need a RAG chatbot for a sports rulebook, business knowledge base, policy documents, manuals, regulations, or research documents, the goal is the same: retrieve relevant evidence and generate reliable, grounded answers.
Send me a Message Today!
The Tech: RAG chatbot, RAG development, RAG optimization, RAG audit, RAG evaluation, document Q&A, AI knowledge base, Python RAG, Claude AI, hybrid search, vector search, reranking, AI implementation, AI agent, embeddings, source citations, grounded answers, document retrieval, dify, llamaindex, qdrant, n8n, lightrag, langgraph, Google Drive
Get to know Sheldon Wisdom
Sheldon Wisdom
AI Automation Specialist Building Smart Business Workflow Systems
- FromUnited Kingdom
- Member sinceJul 2026
- Avg. response time1 hour
Languages
English
I help businesses eliminate repetitive work by designing AI-powered automation systems, workflow integrations, and scalable digital processes. I specialize in connecting platforms through Make.com, Zapier, APIs, webhooks, CRMs, and AI tools to improve operations and productivity. From client onboarding and lead management to internal workflows and reporting systems, I build reliable solutions that save time, reduce manual effort, and help businesses operate smarter.
FAQ
Can you optimise an existing RAG instead of rebuilding it?
If you already have a working prototype, I can start with the current pipeline rather than replace it. I can review failed questions, retrieved context, prompts, document structure, and model outputs to locate the main failure point. The goal is to improve what works without unnecessary rebuilding.
Can you build RAG systems for rulebooks and regulations?
Yes. Rulebooks, policies, regulations, manuals, and other authoritative documents are strong RAG use cases. The system can retrieve the relevant section, keep answers grounded in evidence, and provide citations where appropriate. Exact behaviour depends on your documents and accuracy needs.
What can you improve when my RAG gives wrong answers?
I can investigate document parsing, chunking, metadata, embeddings, retrieval, hybrid search, reranking, context assembly, prompts, and answer generation. I can also turn recorded incorrect responses into an evaluation set so changes are tested against known failures instead of judged only by demos.
Can you work with Claude and Python?
Yes. This project uses Claude and lists Python as a required skill, so this service can support Claude-based RAG implementations and Python pipelines. I prefer your existing architecture where practical, then change only components that evidence shows are causing the problem.
What do you need before starting a RAG optimisation?
Send the current architecture or codebase, sample source documents, several questions that produced incorrect answers, and the expected answers. If you record failures, include that data too. This lets me diagnose the system from evidence and recommend the highest-value next step before changes.
