TigrouMeow

Meetings generate lots of text but too few clear next steps. Rather than asking an LLM for a vague summary, feed it the transcript with a focused goal: extract discrete action items, assign owners (if mentioned), estimate due dates or urgencies when possible, and return a machine-readable list you can import into your task tracker.

Prep the transcript

Before you call the model, clean the input: include speaker labels and timestamps if available, remove filler noise that confuses intent (“um,” “like”), and split very long transcripts into topic-based chunks. Smaller, coherent chunks give the model better context and reduce hallucination risk from unrelated parts of the meeting.

Use structured prompts and templates

Ask the LLM to output a specific format (JSON, CSV, or simple YAML) with fields like “task_text”,”owner”,”due_suggestion”,”priority_reason”,”source_timestamp”. Example instruction: “From the following text, extract action items as JSON objects. Populate owner when someone is explicitly assigned; if no owner is given, leave null. Include the timestamp where this request was made.” A clear format makes downstream automation reliable.

Prioritize and add confidence signals

Tell the model how to prioritize (e.g., deadline, risk, dependencies) and ask it to include a confidence score or short justification for each suggested priority. That way you can surface high-confidence actions automatically and flag lower-confidence items for human review.

Validate and integrate

Don’t rely on the output verbatim. Run simple validation checks (owners match participant list, due dates are reasonable, no empty tasks) and present low-confidence items back to meeting participants for confirmation. Also consider privacy: redact sensitive data before sending transcripts to third-party LLMs or use on-premises models if confidentiality is required.

Retrieval-augmented generation (RAG) doesn’t have to live in the cloud. If your priority is privacy, low latency, or working offline with a small corpus, you can assemble a practical local RAG pipeline on a laptop using open-source tools and a few simple heuristics.

Quick checklist

Chunk documents into 500–1,500 token windows with 20–30% overlap and preserve simple metadata (filename, page or paragraph id). Overlap helps answers that span chunk boundaries and metadata lets you show provenance alongside any generated output.

Create embeddings with a local or hosted model (for example, a sentence-transformer model or a hosted embedding API) and store vectors in a lightweight local index such as FAISS or Chroma. Use cosine similarity for ranking and keep top-k retrieval configurable (start with k=4–8) so you can tune recall vs. prompt length.

When assembling prompts, include only the most relevant chunks plus brief citations (file + chunk id or short excerpt). Prefer an extract-then-summarize pattern: ask the model to extract facts from retrieved chunks first, then synthesize. Apply a relevance threshold or similarity score cutoff to avoid feeding low-quality context into the LLM.

Operational tips: cache embeddings for unchanged files, reindex incrementally when documents change, and monitor recall by sampling queries and checking whether expected source chunks appear in top-k. If latency matters, run embeddings in batches and use approximate nearest-neighbor settings (e.g., IVF or HNSW) to trade a little accuracy for much faster searches.

Security and cost: encrypt or ACL-protect your vector store, keep API keys out of local files, and run local models if regulatory or budget constraints require it. Finally, iterate: tune chunk size, overlap, k, and the prompt template against a small labeled set of queries until results are consistently useful.

LLMs are great at fluent answers, but that fluency can mask errors. Instead of treating the model as an oracle, design a lightweight pipeline that retrieves, constrains, and verifies answers before you surface them to users. Below are actionable steps you can implement today.

1) Retrieve first, generate second

Start every query with a targeted retrieval step (semantic search over your docs, a web search API, or a curated knowledge base). Pass only the most relevant passages to the model and ask it to base its answer on those passages, quoting sources verbatim when possible. This reduces the model’s need to guess and gives you anchors for later verification.

2) Constrain outputs and require provenance

Use precise output schemas (JSON with fixed fields) and explicit instructions: “If unsupported by the retrieved text, respond: ‘Insufficient evidence.’” Encourage the model to include exact citations (document ID, paragraph snippet, or URL) for each factual claim. Lower sampling randomness (temperature) for deterministic outputs.

3) Automate sanity checks and fallback strategies

After generation, run automated validators: regex or type checks, numeric range checks, simple unit tests for code, and a cross-check against a second retrieval or a different model. If checks fail, either re-run with a tighter prompt, escalate to a fallback (e.g., mark as unverifiable), or route to a human reviewer.

4) Monitor, log, and iterate

Log inputs, retrieved contexts, model outputs, and validator results so you can analyze failure modes. Track the kinds of claims that fail most often and add examples to your prompt or update your retrieval index. Over time, build lightweight heuristics (confidence scores, trust tags) to decide which answers can be surfaced automatically and which need human review.

Quick checklist: always retrieve context, require citations, use strict output schemas, run automated validators, and keep humans in the loop for edge cases. Implementing these steps will markedly reduce hallucinations while preserving the productivity benefits of LLMs.

Alors voici la liste des trucs dont j’ai besoin :

• salade
• tomates
• andives
• anchois

Voilà, merci !