RAG (retrieval-augmented generation)
A pattern where the model answers after retrieving relevant chunks from your documents, CRM notes, or knowledge base, instead of relying only on weights baked in at training time.
Michal Juhas · Last reviewed May 2, 2026
Who this is for
TA enablement, sourcing leads, and recruiters building shared context so assistants stop improvising employer facts.
In practice
- Version files:
interview-rubric-v3.mdbeats endless "final_FINAL" docs. - Write for retrieval: short sections, clear headings, one idea per paragraph.
- Tag sensitivity: separate public tone from internal-only legal drafts.
Where it breaks
If nobody deletes outdated comp bands or old policy PDFs, RAG confidently cites wrong guidance. Ownership matters more than embedding math.
From recent workshops
Recruiting and sourcing sessions keep praising Markdown knowledge bases and project folders over one long chat thread. That is RAG culture even before you buy a vector database: structured sources, explicit retrieval step, human editor.
Long chat versus RAG
| Pattern | Strength | Weakness |
|---|---|---|
| Long thread memory | Convenient | Drift, hard audit |
| RAG from files | Grounded, portable | Needs curation |
| Hybrid | Best of both | More moving parts |
Related on this site
- Blog: What is AI-native work?
- Tools: Claude
- Guides: Talent acquisition managers
- Membership: Become a member
Frequently asked questions
What problem does RAG solve for TA teams?
It anchors answers in internal text: employer brand lines, interview rubrics, visa rules summaries you already approved. That cuts generic "AI slop" and gives reviewers a footnote path to sources.
Is a folder of Markdown files RAG?
It can be the knowledge side. Full RAG also needs retrieval (search, embeddings, or hand-picked links) plus a prompt that tells the model to cite or quote those chunks. Many workshop setups start with organized Markdown before they touch vectors.
How is RAG different from pasting a long PDF into chat?
RAG selects the right slices for each question, respecting context limits. Blind paste dumps noise and burns tokens. Quality retrieval is the product decision, not the model pick.
What are common RAG failure modes in recruiting?
Stale documents, wrong chunk boundaries (half a table), mixed languages, or PII in files that should never hit a vendor. Retrieval can also return the wrong policy version if filenames are unclear.
Does RAG remove the need for verification?
No. Models still misread chunks or blend two policies. Pair RAG with the habits in hallucination, especially for numbers and URLs.
Where should we start without engineers?
Curate ten canonical Markdown files: tone, outreach patterns, intake questions, scorecard definitions. Link them from a Gem or project instructions. Read How to use AI in recruiting while you build the library.
When is full vector search worth it?
When files exceed what you can manually pick per req, or when multiple teams need the same corpus with fast updates. Until then, good folder hygiene plus semantic search thinking may be enough.