Polish documentation copy (#98)

docs-site/content/docs/concepts/context-as-code.mdx

@@ -1,23 +1,23 @@
 ---
 title: Context as Code
-description: Treat analytics context like code — version it, review it, merge it.
+description: Treat analytics context like code - version it, review it, merge it.
 ---
 
 ## The idea
 
-dbt proved that analytics transformations belong in version control. Before dbt, SQL lived in BI tools, scheduling systems, and spreadsheets — scattered, unreviewed, impossible to audit. "Analytics as code" changed that: put your models in git, review them in PRs, deploy them by merging.
+dbt proved that analytics transformations belong in version control. Before dbt, SQL lived in BI tools, scheduling systems, and spreadsheets - scattered, unreviewed, impossible to audit. "Analytics as code" changed that: put your models in git, review them in PRs, deploy them by merging.
 
-KTX applies the same principle to analytics context. Metric definitions, business rules, join relationships, wiki pages — these are artifacts that determine whether an agent produces correct results. They change over time. They need review. They need history. They need to be treated like code.
+KTX applies the same principle to analytics context. Metric definitions, business rules, join relationships, wiki pages - these are artifacts that determine whether an agent produces correct results. They change over time. They need review. They need history. They need to be treated like code.
 
 A KTX project is a git repository. Semantic sources are YAML files. Wiki pages are Markdown files. Changes are commits. Updates are pull requests. Deployment is a merge. The entire lifecycle of your analytics context follows the same workflow your team already uses for dbt models, application code, and infrastructure.
 
 ## Auto-ingestion
 
-Most analytics context already exists — it's in your dbt manifests, LookML models, Metabase questions, and team Notion pages. KTX pulls from these sources automatically through adapters.
+Most analytics context already exists - it's in your dbt manifests, LookML models, Metabase questions, and team Notion pages. KTX pulls from these sources automatically through adapters.
 
 An ingestion run works like this:
 
-1. **Adapters extract metadata.** Each configured source — dbt, LookML, Metabase, MetricFlow, Notion, or your live database — provides structured metadata about models, metrics, dimensions, questions, and documentation.
+1. **Adapters extract metadata.** Each configured source - dbt, LookML, Metabase, MetricFlow, Notion, or your live database - provides structured metadata about models, metrics, dimensions, questions, and documentation.
 
 2. **The LLM agent reconciles.** KTX doesn't blindly overwrite existing context. An LLM agent compares incoming metadata against your current semantic sources and wiki pages. It decides what to create, what to update, and what to leave alone. If your dbt project added a new model, the agent writes a new semantic source. If a Metabase question references a metric you've already defined, the agent skips the duplicate.
 
@@ -66,17 +66,17 @@ metadata, and documentation updates are ready for review each morning.
 
 Once merged, agents querying through the KTX CLI see the updated context immediately. No deployment step, no cache invalidation, no restart. The files are the source of truth, and agents read them on every request.
 
-This workflow gives you the same review guarantees you have for dbt models. No semantic source reaches production without a human approving it. But unlike maintaining context manually, the heavy lifting — discovering new tables, drafting source definitions, extracting business rules from documentation — is done by the ingestion agent. You review and approve. You don't write from scratch.
+This workflow gives you the same review guarantees you have for dbt models. No semantic source reaches production without a human approving it. But unlike maintaining context manually, the heavy lifting - discovering new tables, drafting source definitions, extracting business rules from documentation - is done by the ingestion agent. You review and approve. You don't write from scratch.
 
 ## Feedback loops
 
 Context improves over time through two feedback channels.
 
-**Analyst corrections.** When an analytics engineer spots something wrong — a measure formula that doesn't match the business definition, a join that should be `many_to_one` instead of `one_to_many`, a wiki page that's out of date — they edit the YAML or Markdown directly and commit. These corrections become part of the project's git history, and the next ingestion run respects them. If you manually fix a measure definition, KTX won't overwrite it on the next ingest.
+**Analyst corrections.** When an analytics engineer spots something wrong - a measure formula that doesn't match the business definition, a join that should be `many_to_one` instead of `one_to_many`, a wiki page that's out of date - they edit the YAML or Markdown directly and commit. These corrections become part of the project's git history, and the next ingestion run respects them. If you manually fix a measure definition, KTX won't overwrite it on the next ingest.
 
-**Agent feedback.** When an agent queries the semantic layer and gets unexpected results — a query that returns no rows because of a bad filter, a join path that produces duplicated results — it can flag the issue. These signals feed back into the context: wiki pages can note known data quality issues, and source definitions can be tightened with better filters, join paths, or grain declarations.
+**Agent feedback.** When an agent queries the semantic layer and gets unexpected results - a query that returns no rows because of a bad filter, a join path that produces duplicated results - it can flag the issue. These signals feed back into the context: wiki pages can note known data quality issues, and source definitions can be tightened with better filters, join paths, or grain declarations.
 
-Each of these channels makes the next ingestion cycle better. Analyst corrections teach the system what your team considers authoritative. Agent feedback surfaces gaps in coverage. Context is not a static artifact — it's a living system that converges toward accuracy with every iteration.
+Each of these channels makes the next ingestion cycle better. Analyst corrections teach the system what your team considers authoritative. Agent feedback surfaces gaps in coverage. Context is not a static artifact - it's a living system that converges toward accuracy with every iteration.
 
 ## Deterministic replay
 
@@ -84,9 +84,9 @@ Every ingestion session in KTX produces a full transcript: every tool call the L
 
 This matters for three reasons.
 
-**Debugging.** When a semantic source looks wrong — the grain is off, a join points to the wrong table, a measure formula doesn't match the business definition — you can trace it back to the ingestion session that created it. The transcript shows exactly which adapter provided the input, how the LLM interpreted it, and why it made the decision it did. You don't have to guess.
+**Debugging.** When a semantic source looks wrong - the grain is off, a join points to the wrong table, a measure formula doesn't match the business definition - you can trace it back to the ingestion session that created it. The transcript shows exactly which adapter provided the input, how the LLM interpreted it, and why it made the decision it did. You don't have to guess.
 
-**Trust.** Analytics teams need to trust the context that agents consume. Deterministic replay means you can verify any part of the context layer by re-examining the session that produced it. If a stakeholder asks "where did this revenue definition come from?", you have a complete audit trail — from the dbt manifest entry, through the LLM's reconciliation logic, to the YAML file that was written.
+**Trust.** Analytics teams need to trust the context that agents consume. Deterministic replay means you can verify any part of the context layer by re-examining the session that produced it. If a stakeholder asks "where did this revenue definition come from?", you have a complete audit trail - from the dbt manifest entry, through the LLM's reconciliation logic, to the YAML file that was written.
 
 **Reproducibility.** Because ingestion sessions are recorded as structured transcripts (tool calls and responses, not just logs), they can be replayed for testing and validation. If you change your ingestion configuration or upgrade the LLM, you can replay previous sessions to see how the output would differ. This gives you a safety net for changes that affect how context is generated.