u/xrobotx

RAGless is a semantic retrieval system that answers questions about your documentation, without using an LLM at runtime.

Most Q&A systems today are built on RAG: retrieve some context, send it to a language model, generate an answer. RAGless takes a different approach. During ingestion, an LLM converts your documents into a comprehensive set of Question & Answer pairs — automatically covering the full breadth of the source material. At query time, the user's question is matched semantically against those pre-generated questions — and the corresponding answer is returned directly, with no generation step.

The result is a system that is fast, deterministic, and hallucination-free by design.

What it does For closed-domain use cases, the generation step in RAG adds latency, cost and hallucination risk without adding much value — the answer is already known. RAGless removes it.

Pipeline: LLM generates Q&A pairs from your documents at ingestion (runs once) → question variants are embedded and stored in Qdrant → at query time, scores are aggregated by answer_id across Top-K results → pre-written answer is returned.

Target audience Engineers building customer support tools, internal knowledge bases, or documentation systems where answers are predefined. Production-ready for closed-domain use cases. Not a replacement for RAG when open-ended generation is needed.

Comparison	RAG	RAGless
LLM at query time	Yes	No
Hallucination risk at query time	Present	None
Runtime cost	Per query	Almost Zero
Output	Generated	Pre-written
Best for	Open-ended Q&A	Closed knowledge bases

The core difference from standard semantic search: RAGless matches question-to-question (not question-to-document), and aggregates scores across multiple variants of the same answer — more robust than single-hit Top-1 retrieval.

GitHub: github.com/EmilResearch/RAGless

Open to feedback — happy to answer questions.

If you find it useful, a ⭐ on GitHub is appreciated.

What it does

RAGless is a semantic retrieval system based on Question-to-Question matching. At ingestion, an LLM generates multiple question variants per answer (3–5) and each variant gets its own embedding. At query time, the user question is embedded, Top-K nearest question variants are retrieved, and scores are aggregated by answer_id — the answer with the highest aggregated score wins.

Threshold logic uses two gates: minimum aggregated score (default 0.70) plus a fallback on the best single-hit score (0.82), to avoid false negatives when only one variant makes it into Top-K. Embeddings use asymmetric task types (RETRIEVAL_DOCUMENT at ingestion, RETRIEVAL_QUERY at runtime).

Target audience

Researchers and engineers evaluating retrieval architectures for closed-domain FAQ systems where the answer space is finite and predefined. Production-ready for that scope. Not intended for open-ended generative Q&A.

Comparison

Standard RAG: retrieve document chunks → LLM generates an answer. RAGless: retrieve pre-generated question variants → return the pre-written answer. The generation step is eliminated entirely. Compared to dense passage retrieval (DPR) and similar approaches, RAGless operates at the question level rather than the passage level, which improves precision for FAQ-style retrieval at the cost of flexibility.

GitHub: github.com/EmilResearch/RAGless

Open to feedback — happy to answer questions.

If you find it useful, a ⭐ on GitHub is appreciated.