KKissan Ki Pehchan
Assurance

Model, Prompt and Knowledge Operations

Defines registries, promotion, shadowing, canaries, drift monitoring, rollback and controlled improvement.

BlueprintVersion 0.25 Aug 2026

Versioned assets

Maintain registries for:

  • reasoning, speech, vision, embedding and reranking models;
  • system prompts and structured output contracts;
  • workflows and tool definitions;
  • documents, chapter summaries and indices;
  • policy rules and calculators;
  • evaluation sets and scoring code;
  • mobile, API and infrastructure releases.

Model record

Each model entry should include provider, exact identifier or checkpoint, licence, release date, input/output modes, context limits, serving configuration, benchmark results, subgroup results, cost, latency, data-handling terms, approval status and rollback target.

Promotion path

Candidate
→ offline benchmark
→ expert review
→ safety and adversarial suite
→ shadow mode
→ limited canary
→ controlled expansion
→ approved production

Promotion is based on demonstrated system-level improvement, not novelty or vendor ranking.

Prompt and workflow changes

Treat prompts as controlled software:

  • review diffs;
  • run regression and safety suites;
  • test structured output validity;
  • shadow material changes;
  • record approver and rationale;
  • support instant rollback.

Knowledge operations

Document updates trigger parsing, chapter/section generation, metadata checks, indexing, retrieval tests and publication approval. Withdrawal must immediately suppress affected chunks and identify impacted prior cases.

Monitoring and drift

Track:

  • speech error by terminology and subgroup;
  • image and diagnosis performance by crop and district;
  • retrieval miss patterns;
  • source-age and authority distribution;
  • model calibration;
  • policy-rule coverage;
  • farmer and reviewer correction patterns;
  • and provider behaviour or cost changes.

Feedback loop

Feedback becomes an improvement candidate only after validation. Separate:

  • user dissatisfaction;
  • transcription correction;
  • factual correction;
  • new evidence;
  • reviewer disagreement;
  • policy change;
  • and confirmed outcome.

Do not fine-tune or alter retrieval automatically from raw thumbs-up/down data.

Rollback

Rollback plans must cover model routing, prompt, workflow, source publication, index, policy rule and application version. Preserve compatible schemas or provide migrations.