llm-connect/workplans/llm-connect-WP-0004-adaptive-cost-quality-routing.md

LLM-WP-0004 — Adaptive Cost-Quality Routing

status: todo owner: llm-connect repo: llm-connect created: 2026-05-17 depends-on: LLM-WP-0003 (RoutingPolicy primitive)

Purpose

Provide reusable primitives that let a consumer route each task to the cheapest model whose observed output quality clears a per-task bar, with the local Claude Code session (ClaudeCodeAdapter) available as the baseline-quality oracle. The current RoutingPolicy (LLM-WP-0003) is static: rules and cost caps are hand-authored. This workplan adds the observation, grading, and adaptive-selection layer that learns which model is good enough for which task type.

Demand signal: infospace-bench is about to scale from one-chapter smoke runs to multi-chapter and full-book infospace generation across multiple workflow stages (summarise, extract entities, extract relations, evaluate). Each stage has very different quality / cost trade-offs, and the consumer needs llm-connect to pick the right model per stage instead of hard-coding one model for the whole run.

Scope guardrails (read this before adding tasks)

llm-connect ships primitives, not consumer policy.

In scope here:

• Data models for quality observations and grading results
• A reusable observation ledger (persistent, append-only, file-backed)
• A BaselineGrader that pairs a baseline adapter with a candidate adapter and emits a structured quality delta
• A small built-in grader catalogue (exact-match, embedding similarity, LLM-as-judge wrapper)
• An AdaptiveRoutingPolicy that extends RoutingPolicy by consulting the ledger to pick the cheapest adapter whose observed quality for a task type still clears a configured threshold
• A shadow-mode wrapper adapter for collecting observations in production without changing caller behaviour

Out of scope (belongs in consumer repos):

• Task-type taxonomy (callers name their tasks)
• Quality thresholds per task type (callers set their own bars)
• Choice of baseline (callers wire whichever adapter they trust)
• When to re-grade (callers decide; this repo just exposes ledger TTL and refresh helpers)
• Cost accounting for billing or budgets beyond a per-call estimate

GAAF notes

All additions are Functional-layer per GAAF-2026. Core stays untouched. Each new module gets a functional contract doc under contracts/functional/. Maturity on release: Beta — infospace-bench is the first known consumer; the API may shift before any second consumer (inter-hub, markitect) adopts it.

Tasks

T01 — Quality observation data model + ledger

ID	Title	Priority	Status
T01	QualityObservation dataclass: task_type, adapter_id, model_id, cost_usd, quality_score (0..1), latency_ms, tokens_in, tokens_out, baseline_adapter_id, recorded_at, tags	high	todo
T02	QualityLedger append-only JSONL store with file-locked writes, configurable path, simple query helpers (by_task_type, recent, mean_quality)	high	todo
T03	TTL helpers: prune_before(timestamp) and is_stale(observation, max_age) so callers can refresh observations without re-reading the whole ledger	medium	todo
T04	Functional contract doc for the ledger schema and the field semantics of quality_score	medium	todo
T05	Tests: round-trip, concurrent writes, query helpers, TTL, malformed-line resilience	high	todo

T02 — Baseline grader

ID	Title	Priority	Status
T06	GradingResult dataclass: quality_score, notes, grader_id, baseline_response, candidate_response	high	todo
T07	BaselineGrader protocol: .grade(baseline_adapter, candidate_adapter, prompt, run_config) → GradingResult; built-in concrete PairedGrader runs both calls and delegates to a Judge	high	todo
T08	Judge protocol + three built-ins: ExactMatchJudge, EmbeddingSimilarityJudge (uses an embedding adapter), LLMJudge (uses a third adapter with a fixed rubric prompt)	high	todo
T09	Functional contract doc covering judge bias caveats (length bias, format bias, position bias for LLMJudge)	medium	todo
T10	Tests: each judge against canned inputs, grader emits stable result with both responses preserved, deterministic seed for LLMJudge rubric	high	todo

T03 — Adaptive routing policy

ID	Title	Priority	Status
T11	AdaptiveRoutingPolicy extends RoutingPolicy: given task_type + quality_floor + ledger, returns the cheapest adapter whose observed mean quality clears the floor over a configurable window	high	todo
T12	Tie-breaking: when two adapters meet the floor, prefer lower observed cost; if still tied, prefer the explicitly-preferred adapter from the underlying static rules	medium	todo
T13	Cold-start behaviour: when no observations exist for a (task_type, adapter) pair, fall through to the static RoutingPolicy.resolve result so the system stays usable on day zero	high	todo
T14	Functional contract doc; document the trade-off between sample size and freshness	medium	todo
T15	Tests: floor enforcement, tie-break, cold-start, window-size effect, fallback chain	high	todo

T04 — Shadow-mode observation wrapper

ID	Title	Priority	Status
T16	ShadowingAdapter wraps a candidate adapter; on each call, also invokes the baseline adapter (sync or via a thread pool), grades, and appends to a QualityLedger	medium	todo
T17	Sampling: caller-configurable fraction (shadow_rate=0.1 means grade one call in ten) so production load is not doubled	medium	todo
T18	Failure isolation: shadow errors never affect the candidate response returned to the caller; failures are logged but not raised	high	todo
T19	Functional contract doc	low	todo
T20	Tests: candidate response always returned even when baseline raises, ledger gets exactly shadow_rate × calls entries (within tolerance), sync vs async modes	high	todo

T05 — End-to-end example + integration test

ID	Title	Priority	Status
T21	Example script: route a small fixture batch through three candidate adapters (one OpenRouter cheap, one OpenRouter mid, ClaudeCodeAdapter as baseline), grade each, populate ledger	medium	todo
T22	Integration test with mocked adapters covering: cold-start → static fallback → first observations → adaptive selection converges to the cheapest qualifying adapter	high	todo
T23	Brief consumer-integration guide in docs/ showing how infospace-bench (or any caller) wires task-type-per-stage into the adaptive policy	medium	todo

Risks and open questions

• Judge bias. LLMJudge has known biases — length, position, format, self-preference when the judge model is the same family as a candidate. The contract must document these and recommend pairing with at least one non-LLM judge for calibration.
• Baseline cost in shadow mode. ClaudeCodeAdapter is not per-call billed (it shells out to a local subscription session), but every shadow call still consumes wall-clock and rate-budget. Sampling is load-bearing, not optional.
• Non-stationarity. Provider model updates, prompt changes, and template edits all silently invalidate prior observations. Plan for a prompt_fingerprint tag on observations so the ledger can be filtered to a coherent regime.
• Scope creep. "Pick a model" is one decision; "decide whether the task is worth doing at all" is another. The latter is consumer policy. Keep this workplan firmly on the former.
• Privacy. Observations contain prompt and response text by default. Add a redact: Callable[[str], str] hook on the ledger writer so sensitive callers can store hashes / digests instead.
• API-vs-CLI baseline parity. A consumer that grades against ClaudeCodeAdapter (CLI) but later switches to a Claude API adapter may see quality drift that's actually transport drift. Document this.

Exit criteria

• QualityLedger round-trips observations and exposes the documented query helpers
• BaselineGrader produces deterministic GradingResult objects for at least one non-LLM judge and one LLM judge given canned inputs
• AdaptiveRoutingPolicy.resolve(task_type, quality_floor=0.8) returns the cheapest adapter whose mean quality over the configured window clears the floor, with a documented cold-start fallback
• ShadowingAdapter never alters the candidate response and respects the sampling rate within statistical tolerance
• End-to-end example produces a ledger with at least three adapters per task type and the integration test shows convergence to the cheapest qualifying adapter
• Functional contracts published for the new data models, the grader protocols, and the adaptive policy

Consumer-side follow-up

infospace-bench will need a small companion workplan (IB-WP-NNNN) to:

• Replace direct OpenRouterAssistedGenerationAdapter use with a task-type-tagged route through AdaptiveRoutingPolicy
• Define its task-type taxonomy (summarize-source, extract-entities, extract-relations, evaluate-entity, synthesize-report)
• Pick a baseline adapter (most likely ClaudeCodeAdapter) and a quality threshold per stage
• Wire the shadow-mode wrapper for the first multi-chapter run so the ledger fills up while real generation proceeds

That workplan should be drafted after T01–T03 of this workplan land, so that the consumer-side wiring is anchored in a stable llm-connect API.