Prompt Engineering & Pipeline Optimization
Golden rules for deterministic AI outputs, minimal token waste, and zero hallucinations.
Prompt Engineering & Pipeline Optimization
To ensure your scheduled workflows run with mathematical consistency, minimal token overhead, and zero hallucinations, adhere to these proven design principles:
1. Explicit Output Contracts
LLMs perform best when output shape and constraints are strictly declared. Always specify the exact structure expected:
- Suboptimal Prompt: “Summarize the morning tech news.”
- Optimal Prompt: “Provide a 3-bullet summary. Each bullet must feature a bold headline, a 2-line explanation, and the estimated market impact percentage. Output pure markdown.”
2. Declarative Negative Constraints
Declaring what not to do is just as critical as defining what to do:
- “Avoid introductory conversational pleasantries (‘Here is your summary…’). Begin immediately with the structured data.”
- “Exclude unverified rumors or unsourced tabloid claims.”
3. Realistic Polling Schedules
- For pricing and inventory monitoring, polling every 3 to 6 hours or once daily provides maximum signal without overloading endpoints.
- For news digests, scheduling executions early in the workday (e.g. 07:30 or 08:00 UTC) ensures morning briefings are fresh and ready for action.
4. Leverage Sliding Memory
Tishtar automatically preserves the state of prior executions. Do not instruct the prompt to re-parse entire historical catalogs. Rely on Tishtar’s built-in deduplication engine to isolate only novel diffs.