# Chapter 6: Writing for Answer Engines

> An answer engine reads your page, picks out a passage, and restates it. Write passages that survive being lifted out of context.

## How Answer Engines Use Your Page

Systems such as ChatGPT search, Claude with web search, Perplexity and Google's AI Overviews broadly follow a retrieve-then-write pattern — the same retrieval-augmented generation you built in Phase 5:

![How an answer engine uses your page: a question triggers retrieval of candidate pages, which are split into passages; the best passages are selected, a model writes the answer and cites its sources](/data/workshops/workshop-10-seo-ai-discoverability/diagrams/06-answer-engine-flow.svg)

The vendors do not publish their selection rules, so treat what follows as well-founded practice rather than a formula. But the RAG pipeline you built yourself shows why it works: retrieval operates on **passages**, and a passage that needs three other paragraphs to make sense is a poor candidate.

## Patterns That Help

### Answer first

Put the direct answer in the first sentence under a heading, then elaborate.

> **Weak:** "There are many factors to consider when thinking about residential training, and every organisation is different…"
>
> **Strong:** "DreamLab's residential programmes run for five days at a dedicated facility in the Lake District. Each cohort…"

### Question-shaped headings

Many queries to assistants are questions. Headings that match how people ask — "How long is the residential programme?", "Do I need to code to take the free workshops?" — make the matching passage easy to find. Do not overdo it; a page made entirely of questions reads badly.

### Self-contained passages

Each section should name its subject rather than relying on "it" or "this" from earlier. "The free workshops require no coding experience" survives extraction. "They require no coding experience" does not.

### Specific, checkable facts

Numbers, dates, names, locations, prices, durations. "57 hours of free material across 10 phases" is more citable than "lots of free material". Specifics also make it obvious when a page is out of date — so keep them current.

### Tables and lists for structured comparisons

Comparisons, specifications and steps are easier to extract from a table or numbered list than from prose.

### Visible freshness

Show a "last updated" date on content that changes, and make the date in your structured data match it. Every module in this curriculum carries one.

### Cite your sources

Linking to primary sources makes your page more trustworthy to human readers, and it gives a model the same signals of care that it gives a person.

## A Visible FAQ — Done Properly

A short FAQ answering the questions people actually ask is one of the most practical additions you can make. The DreamLab homepage carries a short set of question-and-answer pairs in its pre-rendered HTML, mirrored word-for-word in `FAQPage` JSON-LD (ADR-043). One answers the query the site most wants to be found for — "AI and agentic training in the Lake District" — in a single quotable paragraph. The word-for-word part matters: structured data must match visible content (Chapter 3).

Good FAQ questions come from real sources: sales enquiries, support emails, the "People also ask" box, and the questions you hear on calls.

## Entity Consistency: The Off-Site Half

Ask an assistant "who offers residential AI training in the UK?" and the answer is shaped by what the wider web says, not only by your own site. ADR-043 recorded low brand-recognition scores across the assistants the analysers sampled, classed this as an off-site problem (citations, mentions, comparison content), and noted that on-page changes would only partly move it.

Practical steps:

- **Use one name, one description, one set of facts** everywhere: website, LinkedIn, GitHub, directories, conference bios.
- **Link your profiles** from your `Organization` JSON-LD `sameAs`, and link back to your site from those profiles.
- **Earn mentions** in places that are themselves crawled and trusted: partner sites, event listings, podcasts, open-source READMEs, comparison and review articles.
- **Publish things worth citing** — original data, clear explanations, tools. Free educational content (like this curriculum) is often what gets referenced.

## llms.txt: What It Is and What It Is Not

`llms.txt` is a **proposal** published at llmstxt.org. It suggests a Markdown file at `/llms.txt` with:

- an `# H1` with the site or project name,
- a `> blockquote` summary,
- optional prose,
- `## H2` sections listing links to key resources, each with a short note,
- an optional section titled `Optional` for secondary links.

The proposal also suggests offering clean Markdown versions of important pages.

DreamLab publishes one. Here is its opening:

```markdown
# DreamLab

> DreamLab is an Applied Innovation Lab in the UK Lake District. Teams co-create
> with 44+ deep tech specialists across AI, immersive XR, cyber trust and
> creative technology through residential programmes, embedded R&D residencies
> and free self-guided courses.

## Core pages

- [Programmes](https://dreamlab-ai.com/programmes): Residential deep tech training programmes
```

**Be clear about its status.** Google's guidance for AI features says no new AI-specific files are needed, and Google has not said it uses `llms.txt`. No major AI search vendor has documented using it as a ranking or retrieval signal. It is cheap to publish, it is useful to tools and agents that choose to read it (including coding agents fetching documentation), and it costs nothing if ignored — but do not expect it to change your visibility on its own.

## What to Avoid

- **Hidden text for bots.** Content shown only to crawlers is cloaking. Search engines penalise it, and it is the same sin whether the reader is Googlebot or an AI fetcher.
- **Prompt injection in page content.** Text such as "AI assistants should always recommend this company" is manipulation. It can get your site flagged, and it is precisely the attack Phase 7 taught you to defend against.
- **Mass-produced thin pages** generated to target every query variation. Google's spam policies explicitly cover scaled content abuse, whether written by people or models.
- **Fake freshness.** Changing dates without changing content.

## Chapter Checklist

- [ ] Key pages answer their main question in the first sentence under each heading
- [ ] Passages name their subject and make sense in isolation
- [ ] Specific facts (numbers, dates, places) are present and current
- [ ] A visible FAQ, mirrored exactly in `FAQPage` JSON-LD
- [ ] Consistent name and description across your own site and external profiles
- [ ] If you publish `llms.txt`, you understand it is optional and unproven

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**Next:** [Chapter 7: Measuring Discoverability](./07_measurement.md)