Aiq Customize Prompts Models

Customize AI-Q Prompts and Models

Use this skill when a developer wants to change how an AI-Q agent reasons or which model it uses — by editing a Jinja2 prompt template or by assigning a different LLM to an agent role — usually without touching agent code. AI-Q agent behavior is driven by prompts and config, so most tuning is a template or YAML change. The one exception is adding a brand-new template, which needs a one-line load_prompt wiring in the agent (see the prompt-templates reference).

Start Here

• Confirm the change is prompt or model customization, not new tool/agent logic. For a new retrieval source use aiq-add-data-source; for a new tool use aiq-add-tool.
• Read the authoritative docs and the existing templates/config below first.
• Prefer editing an existing template or config field over adding new machinery.
• Keep the prompt's STRICT citation rules intact, and never hard-code a model name where an llms: ref belongs.
• Keep templates general-purpose: don't hard-code specific queries, domains, or source/tool names — those come from the user's request and the data_source_registry at runtime.

Authoritative References

• docs/source/customization/prompts.md: prompt guide — template inventory, load_prompt(path, name), render_prompt_template(template, ...), the documented template variables, the STRICT citation rules, and "Creating a New Template". Note it does not document every template's variables (e.g. source_router.j2, writer.j2, source_registry.j2) — the .j2 files are authoritative for the variables they actually use.
• docs/source/customization/swapping-models.md: choosing hosted vs. self-hosted NIMs and pointing config at them.
• docs/source/customization/configuration-reference.md: the llms section and each agent's config fields (deep_research_agent, clarifier_agent, …).
• src/aiq_agent/common/prompt_utils.py: load_prompt and render_prompt_template.
• src/aiq_agent/common/llm_provider.py: LLMRole and LLMProvider.configure, which bind a resolved LLM to an agent role (used by the deep research agent).
• Templates to model on: src/aiq_agent/agents/deep_researcher/prompts/*.j2 (orchestrator, planner, researcher, source_router, writer) and src/aiq_agent/agents/clarifier/prompts/*.j2. Other agents have prompts too (e.g. shallow_researcher, chat_researcher) — check src/aiq_agent/agents/*/prompts/.

Longer procedures live in this bundle:

• references/prompt-templates.md: where templates live, how they load and render, template variables, citation rules, and how to edit or add one safely.
• references/model-selection.md: the llms section, per-agent LLM fields, role binding via LLMProvider, and swapping models.

Workflow

1. Identify the target agent and whether the change is a prompt or a model.
2. For a prompt: edit the relevant src/aiq_agent/agents/<agent>/prompts/*.j2 template; keep its variables and citation rules intact (see the references).
3. For a model: add or point an llms: entry in the config and set the agent's role field (e.g. orchestrator_llm, planner_llm, researcher_llm, writer_llm, source_router_llm) to that ref — do not edit Python to swap a model.
4. Keep token cost in mind: prefer reordering static instructions before dynamic content (KV-cache reuse) and a cheaper model for low-stakes roles.
5. Validate (below): lint any changed Python, run the agent's tests, and smoke-run the CLI against the config you changed.
6. Summarize changed files and paste the validation evidence.

Validation

Run the narrowest checks first; broaden only if you touched shared code.

uv run ruff check src/aiq_agent                      # only if you changed Python
uv run pytest tests/aiq_agent/agents/<agent>         # the agent's tests (a prompt-only edit may have none)
./scripts/start_cli.sh --config_file <your config>   # smoke against the config you edited

Expected: the agent loads its templates without a Jinja2 error and runs with the configured model. A bare ./scripts/start_cli.sh uses the fixed default (configs/config_cli_default.yml), so pass --config_file to exercise your change. For a prompt-only edit (which often has no dedicated unit test), the smoke run is the real check; a config/prompt-only change needs no Python lint.

Common Mistakes

• Breaking a template variable or the STRICT citation rules in docs/source/customization/prompts.md, which degrades report grounding.
• Hard-coding specific queries, domains, or source/tool names into a template, which biases the agent toward one task and breaks generalization. Source/domain selection is data-driven (data_source_registry, source_router.j2); keep prompts task-agnostic.
• Hard-coding a model name in Python instead of using an llms: ref and the agent's role field, so the model can no longer be swapped from config.
• Changing an agent's default model when you meant a single sub-role. The deep research agent's default is orchestrator_llm (there is no generic llm field); the clarifier's default is llm. Editing the default shifts every unset role.
• Introducing a large dynamic prefix that defeats KV-cache reuse and raises cost.
• Pointing an agent's role at a model whose entry is not defined in llms:.

Related Skills

• aiq-configure-workflow
• aiq-add-tool
• aiq-add-data-source
• aiq-release-qa
• aiq-prepare-pr