Reference
How the course vocabulary maps onto this grammar, which pieces of common SEO folklore are wrong, and which primary sources the system actually relies on.
| Course concept | Grammar translation | Operational meaning |
|---|---|---|
| Target persona | Audience + conditions | Segment whose task, context, and value can be measured |
| Sales funnel | Journey-state graph | Awareness, evaluation, decision, use, retention, and return paths |
| Search framework | Audience–task–asset–surface–transition sentence | Who needs what, where it is served, and what happens next |
| Core keyword | Representative query | Human-readable label for one dominant intent cluster |
| Secondary keywords | Same-intent variants | Closely equivalent queries that one canonical asset can satisfy |
| Accessory keywords | Adjacent task clusters | Related but independently useful information needs |
| Keyword variations | Lexical normalization | Synonyms, spelling, word order, pluralization, abbreviations, stems |
| LSI keywords | Contextual terms, entities, relations, attributes | Replace the “LSI” bucket with explicit semantic roles |
| One keyword = one page | One dominant intent cluster = one canonical owner | Prevent conflicting page ownership without making a page per wording |
| Intent bucketing | Task-based query clustering | Group queries according to one completion condition |
| Revenue estimates | Probabilistic demand-to-value forecast | Separate assumptions from actual contribution margin |
| SEO timeline estimate | Time-to-event forecast | Track crawl, index, impression, value, and uncertainty separately |
| Reverse-engineering competitors | Competitive retrieval and information-gap analysis | Identify result forms, gaps, authority, and unmet tasks |
| Document relevancy | Query–document task fit | Make the asset understandable and useful for the intended task |
| On-page optimization | Document and search-appearance intervention | Improve relevance, response, evidence, and task completion |
| Pages vs domains | URL-level intent ownership | Assign work and measurement to canonical assets |
| Site architecture | Directed information graph | Paths, depth, hubs, communities, and canonical nodes |
| SEO silos | Topical link communities | Useful internal relationships, not ritual folder structures |
| UX signals | User-outcome telemetry | Task completion, progression, conversion, and experience measures |
| Link building | Independent citation acquisition | Relevant, credible, editorial evidence and prominence |
| Authority metrics | Vendor model outputs | Diagnostic proxies that require calibration to direct outcomes |
| Technical optimization | Retrieval-eligibility engineering | Discover, fetch, render, canonicalize, index, retrieve, display |
| Indexation | One technical state | Inclusion in an index, separated from discovery and display |
| Canonical tags | Preferred representative signal | One signal in a coherent canonical system |
| Keyword cannibalization | Query-to-URL ownership instability | Confirm same-intent competition and business loss before fixing |
| Site speed | Real-user performance system | Improve field experience and value, not a perfect lab score |
| Structured data | Machine-readable eligibility signal | Use supported markup that matches visible content |
| Analytics and tracking | Outcome and intervention model | Connect search exposure to qualified value and learning |
Widely repeated claims the system does not accept.
The course uses “LSI” as a broad label for terms that co-occur around a topic. The grammar replaces this with: These elements should be included because they complete the task, not because a tool produced a term list.
entities
relations
attributes
values
collocations
questions
evidenceUse: A page can be relevant to many query variants. Different words do not automatically require different pages.
one dominant intent cluster
=
one intended canonical ownerTreat lists of factors as hypotheses and diagnostics. The system requires each intervention to state a mechanism and measured outcome.
Do not claim that any analytics metric perfectly reveals a search engine’s internal behavior. Use user behavior as task and business telemetry.
The public toolbar metric disappeared. Link-based systems and link signals remain conceptually relevant. Do not optimize for an obsolete public score.
Use the current E-E-A-T framing cautiously as a quality-evaluation concept, not a single numeric ranking factor. The operational system measures evidence, experience, authorship, sources, transparency, review, and correction practices.
OBSERVATION
directly measured or inspected.
SOURCE FACT
supported by a cited primary source.
INFERENCE
a reasoned interpretation of observations.
FORECAST
an estimated future value with assumptions.
HYPOTHESIS
a testable proposed mechanism.
DECISION
an action chosen under constraints.
UNKNOWN
information still required.The rule library and source guidance should be reviewed when:
Record: