Prompt Engineering
Discover how Mission Agent uses sophisticated prompt design to prevent AI hallucinations and ensure reliable, contextually accurate responses.
The Art of Constrained Creativity
At Mission Agent, we believe that the key to preventing AI hallucinations lies not just in restricting API access, but in crafting prompts that guide AI behavior through intelligent constraints. Our prompt engineering approach combines role assignment, contextual boundaries, and analytical frameworks to ensure reliable, accurate responses.
Below, we demonstrate our methodology using examples from our weather analysis system, showing how specific prompt design choices prevent the AI from making up information while maintaining creative and insightful analysis.
Prompt Analysis Examples
Here are the exact prompts used in our weather analysis system, demonstrating how specific role assignments and constraints prevent hallucinations:
Step 1: Location Analysis Prompt
"You are Ken Burns for real-world locations or A.O. Scott for fictional places. Analyze the book to identify the most specific city/location where significant events occur. Distinguish between real and fictional locations with historical insights or cultural analysis."
Why this works: By assigning specific roles (Ken Burns for real locations, A.O. Scott for fictional), we constrain the AI's perspective and prevent it from making up locations. The prompt focuses on concrete analysis rather than creative interpretation.
Key Design Elements:
- • Role Constraint: Documentary filmmaker vs. literary critic
- • Analytical Focus: "Analyze" rather than "imagine"
- • Specific Task: "Identify the most specific city/location"
- • Validation Method: "Distinguish between real and fictional"
Step 2: Weather Strategy Prompt
"You are A.O. Scott, chief film and literary critic for The New York Times. Determine if the location is real-world and whether to use actual weather data or generate thematically appropriate fictional weather. Base your decision on the story's setting and context."
Why this works: The role of a literary critic provides analytical rigor while the specific instruction to base decisions on story context prevents arbitrary choices. This creates a systematic approach to weather strategy.
Key Design Elements:
- • Authority Role: Established critic with specific expertise
- • Decision Framework: Clear criteria for choosing approach
- • Contextual Basis: "Base your decision on story's setting"
- • Binary Choice: Real data vs. thematically appropriate fiction
Step 3B: Fictional Weather Prompt
"You are a Harvard professor of Literature and Climate Science. Generate weather conditions that reflect the story's themes and atmosphere, including appropriate time systems for fictional universes. Ensure the weather enhances the narrative context."
Why this works: The academic role ensures scholarly rigor, while the dual expertise (literature + climate science) provides both creative and scientific grounding. The instruction to enhance narrative context prevents random weather generation.
Key Design Elements:
- • Academic Authority: Harvard professor with dual expertise
- • Thematic Focus: "Reflect the story's themes and atmosphere"
- • Narrative Integration: "Enhances the narrative context"
- • Systematic Approach: "Appropriate time systems for fictional universes"
Core Prompt Design Principles
1. Specific Role Assignment
Each prompt assigns a concrete role that constrains the AI's perspective and expertise. This prevents the AI from adopting generic or inappropriate personas.
2. Contextual Constraints
Prompts reference specific story elements, data sources, or analytical frameworks to prevent generic responses and ensure relevance.
3. Analytical Focus
Emphasis on analysis rather than creative generation reduces hallucination risk by grounding responses in existing information.
4. Clear Decision Criteria
Each prompt provides specific criteria for making decisions, preventing arbitrary choices and ensuring consistent reasoning.
Universal Application
These prompt engineering principles extend far beyond weather analysis. They can be applied to any domain where AI agents need to provide reliable, contextually accurate information:
Financial Analysis
Role: Financial analyst with specific expertise in market sectors. Constraint: Base analysis on actual market data and regulatory frameworks.
Medical Research
Role: Research scientist with peer-reviewed methodology. Constraint: Reference specific studies and clinical guidelines.
Legal Analysis
Role: Legal scholar with jurisdiction-specific expertise. Constraint: Base conclusions on actual case law and statutes.
Technical Documentation
Role: Technical writer with domain expertise. Constraint: Reference specific APIs, frameworks, and implementation details.