The AI Search Academy is Brilad’s set of guides on how Google, answer engines and AI assistants find, read and choose the pages they show. Each guide explains one part of the process in plain terms and then states what it means for a website. The guides are grouped into three topics.
How AI search works
What happens between a question and an AI answer. Read the overview, or go straight to a topic:
- Retrieval-augmented generation (RAG)
- Query fan-out
- The lost-in-the-middle effect
- Embeddings and cosine similarity
- AI hallucination and grounding
- Context windows and token budgets
- Zero-click searches and multi-source AI answers
- Dense retrieval and re-ranking
- Multi-turn intent and latent constraints
Technical architecture
The parts of a website that decide whether crawlers and language models can use it. Read the overview, or go straight to a topic:
- What is llms.txt?
- AI crawlers and robots.txt
- JSON-LD schema for AI search
- Entity resolution, sameAs and Wikidata
- Content chunking for AI retrieval
- Freshness signals: dates, dateModified and sitemaps
- Tables and data-dense formatting
- IndexNow
- NAP consistency
- Knowledge graphs
- Machine readability and clean HTML
Content that gets cited
What makes a page worth ranking and quoting. Read the overview, or go straight to a topic:
- Information gain
- E-E-A-T for AI search
- First-party evidence and original research
- Trust signals and risk reversal
- Quotability (a Brilad concept)
- Brand mentions and co-occurrence
- Conversion-ready pages (a Brilad checklist)
Applied guides on the blog
The three topics above explain how the systems work. Our Insights blog applies them to particular jobs, platforms and industries, grouped like this:
- Industry guides, from manufacturing and healthcare to hospitality and professional services
- AI search visibility: appearing in ChatGPT, Perplexity, Gemini, Claude, Copilot and AI Overviews
- Answer engine optimization: featured snippets, People also ask, FAQs and structured data
- Technical SEO: crawling, indexing, rendering, sitemaps, redirects and Core Web Vitals
- Platform guides for WordPress, Next.js, Laravel, Shopify, Angular and Vue
- AI development: MCP servers, retrieval chatbots, semantic search and automation scripts
- Content strategy: planning, writing, refreshing and quality-checking pages
- Local and international SEO, including Indian languages and directories
- Authority and digital PR: mentions, links, reviews and profiles
- Measurement and strategy: Search Console, GA4, dashboards, ROI and planning
How these guides are written
Where a guide describes how a system works, it names the source: Google’s documentation, the research paper, or the company’s published crawler list. Where a point is our own working method rather than an established fact, the guide says so. AI search changes quickly, and we revise the guides when the underlying systems change.
If you would rather have this applied to your own site, the AI Search Audit is the usual starting point, and the training workshop covers the same material for in-house teams.