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Accurate RAG chatbot with citations and analytics

Hassan
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  • Description: An on-brand AI assistant that cites your sources, reduces tickets, and surfaces content gaps to fix. 



RAG chatbot that answers from your real content

Summary and value

"Cute AI robot assistant with glowing face holding a marketing message about content creation, value, and digital growth, surrounded by coins and business icons


Delight users with instant, accurate answers sourced from your own docs, blogs, and PDFs. This Retrieval-Augmented Generation (RAG) chatbot reduces support load, increases conversions, and highlights content gaps you can fix.

Tools and stack

  • Ingestion: Python (pymupdf, unstructured), site crawler as needed

  • Vector store: FAISS or Pinecone

  • Orchestration: LangChain for retrieval pipeline

  • Frontend: Next.js, React, Tailwind

  • Observability: basic logging, analytics dashboard

Implementation steps (SOP)

  1. Content collection:

    • Sources: crawl website, import PDFs/Docs; deduplicate and normalize.

  2. Clean and chunk:

    • Processing: remove headers/footers; chunk to 500–800 tokens; store metadata (URL, title).

  3. Embeddings and index:

    • Create: generate embeddings and upsert into vector store; version the index.

  4. Retriever with reranker:

    • Pipeline: top-k retrieval + reranking for higher precision; include metadata.

  5. Prompt design:

    • Guardrails: cite sources; refuse when unsure; tone and brand voice controls.

  6. UI integration:

    • Widget: chat component with history, copy, and source links; mobile-first.

  7. Latency and caching:

    • Speed: short context windows, local cache, stream responses.

  8. Analytics:

    • Track: top queries, fallbacks, unanswered topics → content backlog.

  9. Security:

    • Protect: API keys, CORS, rate limiting; anonymize logs.

Quality and testing checklist

  • Accuracy: ≥ 85% on a curated eval set; each answer cites sources.

  • Latency: p95 < 3s; streaming enabled.

  • Coverage: ≥ 90% of FAQs answered without human escalation.

Deliverables and KPIs

  • Deliverables: API service, web widget, index builder, docs, admin guide.

  • KPIs: Self-serve resolution rate, support ticket reduction, conversion uplift.

Upwork pitch and SEO metadata

  • Pitch: I’ll build a precise RAG chatbot that answers from your content—with citations, analytics, and a slick UI—so customers get instant clarity and your team focuses on higher-value work.

  • Keywords: RAG, chatbot, knowledge base, vector search, LangChain

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