Tópico contém um template completo de prompting para agentes de IA em…
INEMA
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vs. bad outputs, so the agent knows whatt`o aim for or avoid.
Example:
Good Example:
User: “Please summarize the key benefits of theproduct.”
Agent: “The top three benefits are X, Y, and Z (reference data source A). They help users save time, reduce costs, and increase productivity.”
Bad Example:
Agent: “Just buy it; it’s the best.” (No detail or references)
- Formatting
and Output SpecificationsMetadata: If multiple AI outputs will be combined, you might needto return metadata (e.g., timestamps, versioning info, or references used). Structured Output: If the system requires JSON or a specific markup,specify that. Error Handling: Indicate what the agent shoulddoif an error occursor a query can’t be answered.
Example:
Output Format:
Return your final answer as valid JSON with the keys: “summary”, “references”.
If no valid references are f`ound, return an empty list for “references” and note “No additional references.”
.AI Agent Prompting Template 🤖
[ROLE]
- You are the [Name of Agent / Role].
- Your main responsibility is to [Primary Objective].
[CONTEXT]
- System environment: [Briefly describe the multi-agent environment].
- Known details: [Facts, references, previous conversation context].
[TASK]
- Goal: [Specific outcome you want].
- Constraints: [Time limit, length limit, etc.].
- Style: [Tone, formatting guidelines].
- Output format: [Plain text, HTML, JSON, etc.].
[COLLABORATION]
- Other agents: [Who they are, what they provide].
- Interaction: [How and when this agent should interact with them].
[EXAMPLES]
- Good response example: [...]
- Bad response example: [...]
[FINAL REQUIREMENTS]
- QA checks: [Spelling, grammar, factual accuracy].
- Ethical guidelines: [Allowed/Disallowed content].
- Error handling: [Instructions if something goes wrong].
How it works….
1. Role Definition Role/Persona: Explain what this agent is supposed to be or act as (e.g., “You are a financial advisor,” or “You are a marketing specialist”). Expertise Level: Specify the level of expertise expected (e.g., “Expert in legal matters,” “Beginner-friendly instructor”). Primary Objectives: Summarize what the agent does in the multi-agent system. For instance, is it the summarizer, the code generator, the fact-checker, or something else?
Example: Role: You are the “Content Fact-Checker” AI, responsible for verifying the factual accuracy of texts and providing corrections or supporting evidence. 2. Context and Background System Context: Provide the larger setting or scenario that the multi-agent system is operating in (e.g., “A team of AIs collaborating to write blog posts for a tech website”). Shared Knowledge: State any relevant facts, constraints, or previously known information. Relevant Documents/Resources: Indicate which resources are available for referencing (e.g., knowledge bases, FAQs, user manuals).
Example: Context: This multi-agent system is designed to create an in-depth user guide for a new software product. Other agents will contribute research, product details, and marketing best practices. 3. Task Explanation / Goal Core Task: Clearly spell out the objective: “Write a step-by-step tutorial,” “Generate marketing slogans,” “Verify factual data in the product description,” etc. Input Details: Clarify if there is specific user input or data that must be processed. Output Format: Outline precisely how you want the response formatted (bullet points, JSON, full paragraphs, etc.).
Example: Task: Given the product specifications below, generate a 300-word FAQ article in plain English. Keep it accurate and user-friendly. 4. Constraints and Instructions Length or Size: Maximum or minimum character or token count. Tone or Style: Formal, casual, academic, comedic, etc. Do’s and Don’ts: Any rules to follow or things to avoid (e.g., “Do not include speculation,” “Always include references,” “Avoid copyrighted text,” etc.). Time or Resource Limits: If needed for real-time systems, mention performance constraints.
Example: Constraints: Keep responses under 500 words. Maintain a friendly, approachable tone. If uncertain about any facts, ask for clarification from the “Research” agent.
5. Collaboration and Interaction ProtocolOther Agents’ Roles: Summarize what the other agents do, so this agent knows where it fits in the workflow. Message Passing: Indicate whether this agent can or should request additional data from the other agents (e.g., “Query the ‘Research Agent’ for references,” “Ask the ‘Design Agent’ for visuals”). Sequence of Turns: In a multi-agent environment, specify how and when this agent should speak or remain silent.
Example: Collaboration: The “Research Agent” can provide product data. The “Design Agent” can contribute visuals or layout ideas. You (the “Content Fact-Checker”) should correct any inaccuracies in the final text and provide reliable sources if available.
6. Example Prompts and ResponsesTemplates: Show a sample conversation that demonstrates how to respond. Positive/Negative Examples: Provide examples of good
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