LLMO
How LLMs Retrieve Information
By ChatLooker Team · Updated 2026-06-13
When you ask ChatGPT or Perplexity a B2B buying question, the model does not "Google" your site the way a human would. It follows a retrieval pipeline that may draw on frozen training knowledge, a vector index, live web search, or a combination. Understanding that pipeline is the foundation of LLM Optimization for SaaS brands that want to appear in AI recommendations — not just occasional name drops.
What Happens Between Your Prompt and the Answer?
Every AI answer passes through roughly four stages: query understanding, source selection, context assembly, and response generation. The retrieval stage — where sources are chosen — determines whether your content ever reaches the model's context window.
Query understanding and intent classification
The system parses your prompt for intent: factual lookup, comparison, how-to, or recommendation. Category questions like "best CRM for mid-market SaaS" trigger recommendation-style retrieval, which favors structured listicles, comparison pages, and authoritative roundups. Vague prompts may rely more on parametric memory and less on fresh retrieval.
Parametric memory (what the model already "knows")
Large language models encode patterns from training data. Brands frequently discussed in reputable B2B contexts — G2, Gartner mentions, widely cited docs — have stronger parametric presence. You cannot directly edit this layer, but sustained publishing and third-party mentions gradually shift what the model "remembers" about your category position.
Vector retrieval and RAG
Retrieval-augmented generation (RAG) converts queries and documents into embedding vectors — numerical representations of semantic meaning. The system finds chunks whose vectors sit closest to the query vector in high-dimensional space. This is why vector search optimization matters: poorly chunked pages return irrelevant passages or get skipped entirely.
Live web browsing and search APIs
Browsing-enabled modes issue search queries, fetch pages, and extract readable text — often preferring recent, well-structured content. Pages with clear headings, fast load times, and minimal render-blocking JavaScript survive this filter better. Static, agent-readable markdown or clean HTML main content improves extractability.
How Do Different AI Products Retrieve Sources?
Not every assistant uses the same stack. B2B marketers should know the differences when prioritizing LLMO work.
ChatGPT default vs web-enabled modes
In default mode, ChatGPT relies heavily on parametric knowledge with no live crawl. In web-enabled mode, it retrieves current pages — and brand mention patterns can shift dramatically between modes. SaaS teams should test both when auditing visibility; a brand strong in memory-only answers may disappear when fresh retrieval favors recently updated competitor content.
Perplexity and citation-first design
Perplexity is built around retrieval and inline citations. Pages with explicit statistics, clear authorship, and FAQ schema often earn citation slots. For B2B SaaS, being cited — even below the fold — builds trust signals that compound across queries.
Google AI Overviews and traditional index overlap
AI Overviews pull from Google's existing index plus specialized ranking signals. Strong traditional SEO still helps, but answer boxes favor concise, extractable passages. LLMO structure (direct answers, lists, tables) aligns with both AI Overviews and chat retrieval.
What Makes Content Retrievable for B2B SaaS?
Retrieval favors clarity over clever copy. Apply these patterns to product, comparison, and category pages.
Self-contained semantic chunks
Each section under an H2 should stand alone: a buyer reading only that section should understand the point. Vector indexes split pages at heading boundaries; orphan paragraphs without context rarely rank in retrieval.
Explicit entity mentions
Name your product, category, and ideal customer profile in the first 100 words of key pages. Embedding models associate tokens with entities; ambiguity ("our platform") weakens matching for category prompts.
Quotable, extractable facts
Bullet lists, comparison tables, and blockquoted statistics become easy lift targets for models assembling shortlists. Product-led visibility research — including AI share of voice analysis — shows that recommendation slots correlate with extractable "best for" statements more than long narrative prose.
Consistent URL and heading stability
Frequent URL changes and heading rewrites break external citations and fragment embedding indexes. Treat high-intent LLMO pages as stable assets with periodic content refreshes, not disposable campaign landers.
How Should B2B Teams Test Retrieval Performance?
Retrieval testing is empirical. Build a prompt library of 30–50 queries your ICP actually uses, then log which sources appear in AI answers monthly.
Build a prompt taxonomy
Group prompts by funnel stage: awareness ("what is X"), consideration ("X vs Y"), and decision ("best X for [segment]"). Weight decision-stage prompts highest — those drive pipeline.
Log source URLs and brand positions
For each response, note cited URLs, mentioned brands, and whether your brand appears in the top three recommendations. Mention without recommendation is a common gap; see the LLMO guide for why top-3 presence matters more than raw mention rate.
Pair retrieval tests with on-site structure audits
If competitors' docs pages win retrieval for integration questions, publish equivalent depth. If comparison blogs win, build an answer-first comparison hub with structured FAQ. See OpenAI ChatGPT search help and Perplexity search guide for retrieval behavior.
FAQ
Q: Do LLMs retrieve entire web pages or just sections?
A: Most RAG and browsing pipelines chunk pages into passages — often by headings or token limits — and retrieve only the most semantically similar chunks. Full-page ingestion is rare.
Q: Why does my brand appear in one AI tool but not another?
A: Different products use different indexes, training cutoffs, browsing policies, and ranking heuristics. Unified visibility requires optimizing for multiple retrieval paths.
Q: Does JSON-LD help LLM retrieval?
A: Structured data primarily helps crawlers and parsers build entity graphs. Indirectly it improves extractability and entity consistency, which supports retrieval — but it is not a substitute for clear prose structure.
Q: How often should we refresh LLMO content?
A: Quarterly for pillars and high-intent comparison pages, or immediately when product positioning, pricing, or category definitions change.
Key Takeaways
- LLM answers combine parametric memory, vector retrieval, and optional live browsing — your content must fit each path.
- RAG systems retrieve semantic chunks, not whole pages; structure content in self-contained sections.
- Browsing-enabled AI modes can surface different brands than memory-only modes.
- B2B SaaS teams should test retrieval with real buyer prompts and track recommendation rank, not mentions alone.
- Pair technical retrieval optimization with product-led visibility measurement via the LLMO pillar.