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Beta. Prompt fragments are a new feature and the API may evolve based on user feedback. The storage model and injection points are stable. There is no dedicated UI for managing fragments today — they’re managed via the REST API (see Managing fragments).
Prompt fragments let you teach the tap generator about a specific data source — its conventions, rate limits, preferred libraries, required env vars — without having to re-type the same hints into every tap description. Once a fragment is configured, its content is automatically appended to the LLM system prompt whenever the user’s text mentions the fragment’s key or any of its aliases (case-insensitive, word-boundary match). Fragments are per-tenant, stored in MongoDB ({env}-tap-prompt), and cached in memory with write-invalidation. No new dependencies and no schema changes — they piggyback on the same infrastructure as tap configs.

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Where they apply

A matching fragment is injected into every AI flow that touches a tap:
FlowMatched against
Tap script generation (/tap/generate)The plain-English description
Tap script fix (/tap/fix)The diagnosis + error + current script
Post-run script review (/tap/review)The current script
Auto-optimize (/tap/optimize)The current script
Tap brainstorm chat (/tap/brainstorm)The rolling description + full chat history
If a key appears in any of those texts, the fragment’s content is appended to a ## User-provided context block at the end of the system prompt.

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Managing fragments

Fragments are managed via the /api/v1/tap-prompts REST endpoints (GET / POST / DELETE / suggest). There is no dedicated UI surface today — tap creation and editing live in the Catalog tab, and prompt fragments continue to apply transparently to tap creation, brainstorm, auto-fix, and optimize whenever a matching keyword appears. To add a fragment from the CLI or a script: 
curl -X POST http://localhost:8080/api/v1/tap-prompts \
  -H "Content-Type: application/json" \
  -H "x-api-key: <your key>" \
  -d '{
    "key": "AWS",
    "aliases": ["S3", "EC2", "Lambda", "boto3"],
    "content": "Use boto3 for AWS APIs. Read credentials from AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY env vars (don't hardcode). Default region is us-east-1.",
    "enabled": true
  }'
GET /api/v1/tap-prompts lists all fragments; DELETE /api/v1/tap-prompts/{key} removes one.

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Each fragment has

  • Key (required, unique) — the primary match keyword, e.g. AWS, Polygon, Stripe. Matched case-insensitively with \b boundaries so AI won’t accidentally match email or Gmail.
  • Aliases (optional, comma-separated) — additional match keywords. A single AWS fragment can also match S3, EC2, Lambda, boto3, etc.
  • Content (required) — the extra system-prompt text. Typically 2-6 sentences covering library choice, env-var names, rate limits, pagination style, and gotchas.
  • Enabled (default on) — setting it false keeps the fragment in storage but stops it from matching.

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Writing good fragments

Keep them dense and actionable:
  • Lead with the preferred library and the exact env var names the generator should use. Example: Use the stripe Python SDK (pip install stripe). Set stripe.api_key = os.environ.get("STRIPE_API_KEY").
  • State rate limits explicitly so the generator can add throttling up front. Example: Polygon.io free tier is 5 requests per second. Throttle with time.sleep(0.25) or a ThreadPoolExecutor with max_workers=3.
  • Note required headers or auth quirks. Example: SEC EDGAR requires a User-Agent header identifying the requester (e.g. "CompanyName contact@email"). Rate limit: 10 requests per second.
  • Never include secrets or long examples. A fragment over ~2000 characters triggers a soft warning — multiple matching fragments can crowd the LLM context.

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Injection preview in the tap wizard

After Generate Script or a Brainstorm reply, any matching fragment keys appear as chips under the description:
Extra context applied: AWS Polygon
This makes it easy to see what the LLM saw, and catch cases where a fragment matched when it shouldn’t have (or failed to match when it should have).

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HTTP API

The same storage is exposed via REST for scripting and tenant migrations. See Taps API for field-by-field request/response shapes.
MethodPathPurpose
GET/api/v1/tap-promptsList all fragments
GET/api/v1/tap-prompts/{key}Get a single fragment
POST/api/v1/tap-promptsCreate or update a fragment (keyed on key)
DELETE/api/v1/tap-prompts/{key}Delete a fragment
POST/api/v1/tap-prompts/suggestAsk the LLM to draft a fragment content body from { key, aliases, content }

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Performance

The injector caches matched fragments in a per-tenant ConcurrentHashMap with a 60s TTL as a safety net. Writes through the API (create / update / delete) invalidate immediately, so changes take effect on the next LLM call without restart. With zero fragments configured the hot path returns in microseconds — the tap flows pay no measurable cost.

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Multi-tenant

Fragments are fully tenant-scoped. Each tenant’s fragments live in their own {env}-tap-prompt MongoDB collection and their own injector cache entry. A fragment configured on one tenant never leaks into another’s prompts.