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Evidence-grade AI for codes & standards

Honest comparison

RAG vs General AI Chatbots – Honest FAQ

These answers reflect what the AS/NZS AI Code Assistant delivers today. No hype—just the facts engineers ask before adopting compliance-grade AI.

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Top questions

What makes the RAG workflow different?

Direct comparisons against generic chatbots so you can evaluate the workflow without marketing fluff.

1️⃣ Why use this RAG app instead of uploading AS3000 to a general AI chatbot?

  • A general-purpose chatbot can read a single PDF you paste, but it forgets it after the session and may lose context for 500+ page codes.
  • Our app stores your uploaded AS/NZS standards inside Supabase storage, chunks them clause-by-clause, and keeps them ready for every query.
  • Retrieval is deterministic: every answer is tied to a stored clause/table/figure so you can see where it came from.

2️⃣ Does the RAG workflow really improve clause accuracy?

  • Yes. Before generating an answer we run multi-search (BM25, semantic, exact match) on the indexed clauses and tables.
  • The assistant only sees the top chunks + metadata, so the response cites the clause numbers that were actually retrieved.

3️⃣ Does this reduce hallucinations compared to plain generic chatbots?

  • Significantly. The LLM is forced to answer from retrieved chunks and we show the citations in the UI.
  • It can still make mistakes if the wrong clause is retrieved, but it will not invent page numbers out of thin air the way an unconstrained chatbot session can.

4️⃣ Can non‑engineers benefit?

  • Yes. The UI shows the raw clause excerpt plus a plain-English explanation, so you can double-check the source even if you are new to the code.
  • A general chatbot can do this only if you manually paste the clause each time; here the retrieval step is automatic.

5️⃣ Is it faster for daily professional use?

  • Yes. Once a company uploads AS3000/AS3008/etc., the documents stay in your library.
  • Search is instant, invitations let teams share the same corpus, and every answer includes clause, table, exception context—no re-uploading.

6️⃣ Can I upload multiple large standards?

  • Yes. The current uploader accepts PDF files (AS/NZS standards, NCC volumes, manuals, project specs, etc.) up to the configured Max File Size (default 100 MB).
  • Each file is stored with metadata (discipline, company) so you can keep multiple codes or amendments side by side.

7️⃣ Is it better with numbers (mm², IP ratings, clearances)?

  • In practice, yes. Because the raw text chunk is included in the prompt, the LLM quotes the exact table row or note.
  • That reduces the risk of altered figures compared to asking a generic chatbot to recall numbers from training data.

8️⃣ Can it track versions (AS3000:2018 vs Amendments)?

  • Yes—as long as you upload each version (e.g., AS3000-2018.pdf, AS3000-Amd2.pdf). The system stores each file separately, so retrieval tells you which document the clause came from.
  • General chatbots cannot guarantee which edition they “remember.”

9️⃣ When is a plain chatbot enough?

  • If you just need high-level summaries, concept explanations, or you’re pasting a short clause manually, a general chatbot alone is fine.
  • RAG is meant for compliance-grade answers where evidence matters.

🔟 Is the RAG app worth it?

  • Worth it if you are an engineer, consultant, estimator, trainer, or compliance lead who needs traceable, clause-level answers every day.
  • Not worth it if you rarely touch standards or are comfortable with approximate answers.
Fair summary

Balanced takeaways before you decide

  • General chatbots are fine for casual or one-off questions where you paste a small snippet yourself.
  • This RAG stack is better whenever you need clause-level accuracy, citations, and repeatable searches across hundreds of pages.
  • Choose RAG only if you work with codes regularly or need evidence-grade answers; otherwise the extra workflow adds unnecessary overhead.

Ready to see clause-level AI in action?

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