Write Task-Specific Routing Preference Descriptions

Plano's plano_orchestrator_v1 router uses a 1.5B preference-aligned LLM to classify incoming requests against your routing_preferences descriptions. It returns an ordered models list for the matched route; the client uses models[0] as primary and falls back to models[1], models[2]... on 429/5xx errors. Description quality directly determines routing accuracy.

Starting in v0.4.0, routing_preferences lives at the top level of the config and each entry carries its own models: [...] candidate pool. Configs still using the legacy v0.3.0 inline shape (under each model_provider) are auto-migrated with a deprecation warning — prefer the top-level form below.

Incorrect (vague, overlapping descriptions):

yaml

version: v0.4.0

model_providers:
  - model: openai/gpt-4o-mini
    access_key: $OPENAI_API_KEY
    default: true

  - model: openai/gpt-4o
    access_key: $OPENAI_API_KEY

routing_preferences:
  - name: simple
    description: easy tasks      # Too vague — what is "easy"?
    models:
      - openai/gpt-4o-mini
  - name: hard
    description: hard tasks      # Too vague — overlaps with "easy"
    models:
      - openai/gpt-4o

Correct (specific, distinct task descriptions, multi-model fallbacks):

yaml

version: v0.4.0

model_providers:
  - model: openai/gpt-4o-mini
    access_key: $OPENAI_API_KEY
    default: true

  - model: openai/gpt-4o
    access_key: $OPENAI_API_KEY

  - model: anthropic/claude-sonnet-4-5
    access_key: $ANTHROPIC_API_KEY

routing_preferences:
  - name: summarization
    description: >
      Summarizing documents, articles, emails, or meeting transcripts.
      Extracting key points, generating TL;DR sections, condensing long text.
    models:
      - openai/gpt-4o-mini
      - openai/gpt-4o
  - name: classification
    description: >
      Categorizing inputs, sentiment analysis, spam detection,
      intent classification, labeling structured data fields.
    models:
      - openai/gpt-4o-mini
  - name: translation
    description: >
      Translating text between languages, localization tasks.
    models:
      - openai/gpt-4o-mini
      - anthropic/claude-sonnet-4-5
  - name: code_generation
    description: >
      Writing new functions, classes, or modules from scratch.
      Implementing algorithms, boilerplate generation, API integrations.
    models:
      - openai/gpt-4o
      - anthropic/claude-sonnet-4-5
  - name: code_review
    description: >
      Reviewing code for bugs, security vulnerabilities, performance issues.
      Suggesting refactors, explaining complex code, debugging errors.
    models:
      - anthropic/claude-sonnet-4-5
      - openai/gpt-4o
  - name: complex_reasoning
    description: >
      Multi-step math problems, logical deduction, strategic planning,
      research synthesis requiring chain-of-thought reasoning.
    models:
      - openai/gpt-4o
      - anthropic/claude-sonnet-4-5

Key principles for good preference descriptions:

Use concrete action verbs: "writing", "reviewing", "translating", "summarizing"
List 3–5 specific sub-tasks or synonyms for each preference
Ensure preferences across routes are mutually exclusive in scope
Order models from most preferred to least — the client will fall back in order on 429/5xx
List multiple models under one route to get automatic provider fallback without additional client logic
Every model listed in models must be declared in model_providers
Test with representative queries using planoai trace and --where filters to verify routing decisions

Reference: Routing API · https://github.com/katanemo/archgw