Mahal Systems Overview › 3Prompts
Prompts
7 optimizations in 3 sub-categories.
a.Structuring
Clarify structure and intent
- 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.
- 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.
- 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.
- 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.
See also
- 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.
b.Caching
Design for caching and reuse
- i.
Keep the prompt prefix cache-stable
Put static content (identity, rules, tool definitions) before a boundary and per-turn content (date, state, memory) after it; keep the prefix bit-for-bit identical across turns; strip thinking text from persisted history; and track per-turn cache-miss cost.
- ii.
Mask tools instead of removing them
When the tools an agent may use change mid-task (by mode, phase or permission), declare every tool once, keep the definitions byte-stable, and restrict the choice another way: OpenAI's allowed_tools or tool_choice "none", Anthropic's appended tool_removal and tool_addition blocks, a decode-time logit mask or grammar on self-hosted models, and a harness check that rejects calls outside the allowed set.
c.Sizing reasoning effort
Match reasoning effort to the task
- 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.
See also
- 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).