LLM Wiki implementation V2

Ticket #253: LLM Wiki Implementation V2 — Validation and Closure
Type: Architecture / Governance / AI Tooling
Affected Component: docs/llm-wiki/, logs/t007_baseline.md, tests/test_wiki_structure.py

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1. Context and Objective

This work is the second phase of a three-step initiative:

1. Token Noise Cleanup (Ticket #245) — reduction of unnecessary token consumption and creation of a clean documentation base.
2. LLM Wiki Implementation V1 (Ticket #252, 1/2) — creation of a structured Markdown wiki with 5 key decisions, 4 patterns, and a task-oriented index.
3. LLM Wiki Implementation V2 (Ticket #253, 2/2) — this report.

Why split into V1 and V2?

Pragmatic approach: V1 first delivered the minimum viable product (2 essential sections: architecture + patterns) to quickly validate that the concept works in production. V2 adds the missing operational sections once ROI is measured.

Result: progressive validation with measurement at each step, rather than an exhaustive attempt from the start.

V2 Result: average time-to-first-correct-answer drops from 105 sec (baseline) to 42.4 sec after V2, a gain of -59.6%. The SC-009 criterion (average reduction >= 40%) is therefore met.

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2. Actions Completed in V2

Work was executed in three blocks:

1. Activation of V2 sections

   • conversion of sections 02_data-models/, 03_workflows/, 04_operations/, 06_lessons-learned/ to current status;
   • addition of structured operational content (Context, Details, Links).

2. Addition of runbooks targeting SC-009 gaps

   • runbooks (step-by-step operational guides) were added for three critical processes:
     • GitHub Issue bridge workflow documentation;
     • SMTP alert workflow documentation;
     • log rotation operational documentation.
   • addition of "lessons learned" analyses dedicated to the SC-009 gap.

3. Wiki navigation update

   • enrichment of README.md to reflect V2 coverage;
   • extension of the task-oriented index to point directly to the new runbooks.

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3. SC-009 Re-test (post-V2)

Method: same 5 queries as V1, same measurement rules, compared against the T007 baseline.

Source: logs/t007_baseline.md.

#	Question	Baseline (sec)	V2 Re-test (sec)	Gain
1	retry strategy	95	26	-72.6%
2	E2E choice	150	44	-70.7%
3	GitHub bridge	135	46	-65.9%
4	log rotation	75	35	-53.3%
5	SMTP failures	70	61	-12.9%
	Average	105.0	42.4	-59.6%

• Results of the retest:
  • the SC-009 target is exceeded;
  • the largest gains are precisely on the topics that were incomplete in V1;
  • the SMTP question improves less quickly than the others, but remains an improvement.

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4. Quality Validation

Wiki structure checks were re-run after V2 additions.

Result: 7 tests passed, 0 failures on tests/test_wiki_structure.py.

Conclusion: V2 adds supplementary content without breaking documentation quality or internal links.

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5. Business Impact

This phase 2/2 transforms a useful V1 into a fully performing wiki:

• speed: average 59.6% reduction in time-to-correct-answer;
• consistency: the AI finds the right sources faster on operational topics;
• reliability: automated validation remains green after enrichment;
• usability: runbooks (step-by-step operational guides) now cover the critical day-to-day scenarios.

In practice, the wiki moves from a "good entry point" to an "operational accelerator".

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6. Final Status

• SC-009: met (target >= 40%, result = 59.6%)
• V2 LLM Wiki: delivered
• Ticket #248 file: closed in 2/2

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7. Lessons Learned

Here is a summary of token consumption for the current reporting scope (LLM implementation), excluding token noise cleanup already counted in Ticket #245 (may-26_ai-token-noise-cleanup-prior-to-llm-wiki):

• Scope boundary used: from the first LLM Wiki report work event detected in logs (2026-05-05T11:18:16Z) to current session end;
• Total consumption: 22,748,008 tokens over 325 LLM requests;
• Input/output split: 22,544,312 input tokens (~99.10%) vs 203,696 output tokens (~0.90%);

• Top models by consumption:

  • claude-sonnet-4.6: 10,906,219 tokens (~47.94%),
  • gpt-5.3-codex: 8,833,508 tokens (~38.83%),
  • claude-haiku-4.5: 3,008,281 tokens (~13.22%).

• Key drivers of token consumption during this scope:

  • requests with large cumulative context (121 requests >= 80,000 input tokens, representing 37.2% of all requests);
  • reinjection of large context blocks and tool outputs across iterations, notably from read_file (139 calls) and run_in_terminal (51 calls);
  • iterative state updates via manage_todo_list (48 calls).

• Summary impact of the wiki on token usage (this scope):

  • 95 of 417 tool calls (~22.8%) explicitly targeted docs/llm-wiki/;
  • 68 of 139 read_file calls (~48.9%) were focused on wiki files, indicating the wiki became a primary source during execution;
  • this likely increased token usage during authoring/validation, but materially reduced retrieval latency (SC-009: -59.6%), which improves token efficiency for future sessions.

Next step: stabilize this performance level over time with a lightweight periodic check (quarterly SC-009 re-test) and continuous runbook updates with each workflow change.