I built a docs Q&A engine that returns null instead of hallucinating
A developer created a Q&A engine that prioritizes accuracy by returning null for questions without a suitable answer. This engine is designed for privacy-conscious users and operates without external API keys or data transmission. It employs advanced techniques to improve query accuracy while ensuring that incorrect answers are not fabricated.
- ▪The Q&A engine uses a FastAPI service to answer questions based on markdown files.
- ▪It implements a unique approach by returning null instead of providing inaccurate answers.
- ▪The developer faced challenges in improving identifier recognition and handling acronyms without degrading index quality.
DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/sujithkrishnanpk_c9f931/i-built-a-docs-qa-engine-that-returns-null-instead-of-hallucinating-58p6 |
| Publication time | Fri, 29 May 2026 09:36:01 +0000 |
| Retrieval time | 2026-05-29T09:50:00.154Z |
| Last seen | 2026-05-29T09:50:00.154Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 08fE1bikhUvZ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3958163) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sujithkrishnan.p.k Posted on May 29 I built a docs Q&A engine that returns null instead of hallucinating #programming #showdev Every "docs chatbot" today routes user questions through OpenAI. For open-source maintainers, privacy-conscious teams, and air-gapped environments, that's either too expensive or unacceptable. So I built one that doesn't.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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