Mahal Systems Overview › Lever
Prompt
System prompt, instructions, few-shot examples, prompt optimization, skill content. 8 optimizations change this part of an agent.
- 1.b.i
Match the model and its effort to each call site and query class
Pick the model per role or call site rather than one model everywhere, and set its reasoning effort and sampling parameters per site; tune each role's prompt to its model (GEPA raised every model tested at lower cost).
- 2.b.ii
Tell the agent when to use each tool, in context it always sees
For each tool or skill that is under-used or misused, add an explicit trigger rule where the model always sees it (system prompt, AGENTS.md): the situation that calls for the tool, and what to use it instead of (e.g.
- 3.a.i
Add worked examples to the prompt (few-shot), or curated example queries for data agents
Put a few solved examples of the task in the prompt (the input and the exact output wanted), or give a data agent commented example queries for its custom metrics and an index of past queries it retrieves per question.
- 3.a.ii
Avoid few-shot ruts
An agent's own history, and any examples in its prompt, act as few-shot demonstrations: when the context fills with near-identical action-observation pairs (twenty similar items in a row, a retried call), the model tends to copy their surface form instead of judging the current item.
- 3.a.iii
Dynamic context injection
Put the runtime facts the model cannot know into its context each run: today's date and timezone, the working directory, OS and git state, the tools available, the user and locale.
- 3.a.iv
Restructure the system prompt: every requirement once, up front, in named sections; nothing the model doesn't need
Rewrite the system prompt so that each requirement the task has (constraints, the answer format, the completion check, how to work efficiently, the domain procedure) is stated once, up front, in a named section, and remove instructions the model does not need.
- 3.c.i
Self-Discover reasoning structures
Self-Discover has the model compose a reasoning structure once per task type (select useful modules from 39 seed heuristics, adapt them to the task, write them as a step-by-step plan), then follow that cached structure on every instance, so each instance costs one call.
- 9.b.i
Make the final answer reliably extractable: state the format, extract robustly, force an answer at the limit
State the required answer format in the prompt, and make the harness extract it robustly: strip markers, normalize numbers, detect refusals, and force a best-effort answer when the loop ends on a budget rather than returning nothing.