# Infospace-Bench Adaptive Routing Guide

This guide shows how a consumer such as `infospace-bench` can wire task-type
stages into the adaptive cost-quality primitives from `llm-connect`.

## Stage taxonomy

The consumer owns task names and quality thresholds. A first pass for
`infospace-bench` could use:

| Stage | Task type | Suggested floor |
|-------|-----------|-----------------|
| Source chapter summary | `summarize-source` | `0.82` |
| Entity extraction | `extract-entities` | `0.88` |
| Relation extraction | `extract-relations` | `0.86` |
| Entity evaluation | `evaluate-entity` | `0.90` |
| Report synthesis | `synthesize-report` | `0.92` |

These floors are starting points, not library defaults. Raise them for stages
whose errors compound downstream.

## Wiring sketch

```python
from llm_connect.grading import ExactMatchJudge, PairedGrader
from llm_connect.quality import QualityLedger
from llm_connect.routing import AdaptiveRoutingPolicy, RoutingRule
from llm_connect.shadowing import ShadowingAdapter

ledger = QualityLedger("quality-ledger.jsonl")
grader = PairedGrader(ExactMatchJudge())

baseline = claude_code_adapter
cheap = openrouter_cheap_adapter
mid = openrouter_mid_adapter

shadowed_cheap = ShadowingAdapter(
    candidate_adapter=cheap,
    baseline_adapter=baseline,
    grader=grader,
    ledger=ledger,
    task_type="extract-relations",
    adapter_id="openrouter-cheap",
    baseline_adapter_id="claude-code",
    shadow_rate=0.1,
    tags={"prompt_fingerprint": prompt_fingerprint},
)

policy = AdaptiveRoutingPolicy(
    rules=[
        RoutingRule("extract-relations", prefer=baseline, fallback=mid),
    ],
    ledger=ledger,
    adapters_by_id={
        "openrouter-cheap": shadowed_cheap,
        "openrouter-mid": mid,
        "claude-code": baseline,
    },
    window_size=20,
    min_observations=3,
)

adapter = policy.resolve("extract-relations", quality_floor=0.86)
response = adapter.execute_prompt(prompt, run_config)
```

## Operating loop

1. Start with static routing to the trusted baseline or mid-tier adapter.
2. Wrap cheaper candidates with `ShadowingAdapter` at a conservative
   `shadow_rate`, for example `0.05` to `0.1`.
3. Record a prompt fingerprint or template version in `tags` so later prompt
   changes do not mix incompatible observations.
4. Increase `min_observations` for stages with high variance.
5. Let `AdaptiveRoutingPolicy` select the cheapest adapter that clears each
   stage floor.

## Refresh rules

When a provider model, prompt template, or parser contract changes, treat prior
observations as a different regime. Either write to a new ledger, prune old
observations, or filter with a new `prompt_fingerprint` tag before trusting
adaptive selection again.