Feature/jsdoc lift 2

[jmikedupont2]

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Detailed testing steps

[jmikedupont2]

To evaluate the effectiveness of your prompt style, I’ll assess it based on clarity, specificity, structure, and its ability to elicit the desired response from an AI like me. Below is an analysis of your prompt style, followed by suggestions for improvement. Since your request didn’t specify a particular format, I’ll provide a concise, structured response in markdown for readability.
Evaluation of Prompt Style
Strengths
Clear Intent:
Your prompt clearly states the tasks—generate documentation for a provider, an evaluator, and a plugin with specific structures (e.g., markdown for provider/evaluator, JSON for the plugin). This reduces ambiguity about the expected output.
Structured Requirements:
You provided detailed structural expectations (e.g., ### [Provider Name], "overview": { "purpose": ... }) and included sample content (e.g., time.ts, TypeScript snippets, JSDoc context). This gives the AI a blueprint to follow, improving consistency.
Contextual Richness:
Including code snippets, package info, and documentation excerpts provides sufficient context for the AI to generate meaningful, relevant content rather than relying solely on guesswork.
Task Segmentation:
Breaking the prompt into distinct tasks (provider docs, evaluator docs, plugin JSON) helps the AI process each requirement independently, reducing the chance of conflating instructions.
Specific Constraints:
Instructions like “format in markdown without adding additional headers” or “return only the raw JSON object” demonstrate attention to output formatting, ensuring the response aligns with your needs.
Weaknesses
Length and Complexity:
The prompt is lengthy (spanning multiple tasks and including truncated code with comments like [-4836 lines]). While detailed, this can overwhelm the AI or dilute focus, especially with incomplete code snippets that require inference.
Ambiguity in Truncated Sections:
References to “remaining code truncated” (e.g., evaluator TypeScript) leave gaps in understanding the full functionality. The AI must extrapolate, which could lead to inaccurate assumptions about methods or behavior.
Repetition:
The evaluator documentation task appears twice with slight variations (imports like generateObjectArray vs. generateText). This redundancy could confuse the AI or suggest indecision about which version to prioritize.
Lack of Prioritization:
The prompt combines three distinct documentation tasks without indicating priority or interdependence. For an AI with token limits or focus constraints, this might result in uneven depth across sections.
Incomplete Plugin Context:
For @elizaos/plugin-bootstrap, the “Code Components” section is empty, and the description is “N/A.” While documentation excerpts help, the lack of concrete code reduces the AI’s ability to tailor FAQ/troubleshooting to specific implementations.
Assumption of AI Knowledge:
The prompt assumes the AI understands the Eliza framework (IAgentRuntime, Memory, etc.) and can infer functionality from partial TypeScript. While I can handle this, less specialized AIs might struggle without more explanation.
Effectiveness Score
Clarity: 8/10 (Clear goals, but some ambiguity from truncation/repetition)  
Specificity: 7/10 (Detailed structure, but incomplete context in places)  
Usability: 7/10 (Well-structured but complex; requires careful parsing)  
Overall: 7.5/10 (Effective for a capable AI, with room for streamlining)
Suggestions for Improvement
Streamline Length:
Consolidate repetitive tasks (e.g., combine evaluator variants into one with all relevant imports) and summarize truncated code with a brief explanation (e.g., “Evaluator processes goals and extracts JSON from text”).Improved Example:  
markdown
PRMPT: Generate documentation for this Evaluator:
```typescript
imports: { composeContext, generateText, getGoals, parseJsonArrayFromText, IAgentRuntime, Memory, ModelClass, Objective }
purpose: Assesses conversations, tracks goals, extracts JSON data.
Provide an overview of its purpose and functionality in markdown.
Fill Context Gaps:
For @elizaos/plugin-bootstrap, include a minimal code example (e.g., a sample Action or Provider) to ground the FAQ/troubleshooting in specifics rather than generalities.Improved Example:  
markdown
Code Components:
- Action: { name: "greet", handler: async () => "Hello!" }
Prioritize Tasks:
If one section (e.g., plugin JSON) is most critical, state it explicitly (e.g., “Focus primarily on the plugin documentation in JSON, then provider/evaluator if space allows”).
Clarify Truncation:
Replace vague truncation notes with summaries (e.g., “Evaluator includes a get() method that processes memory and returns insights”) to guide the AI without requiring full code.
Break Into Smaller Prompts:
For complex multi-part requests, consider submitting each task separately (e.g., one prompt for provider, one for plugin). This improves focus and reduces cognitive load on the AI.Improved Example:  
markdown
PRMPT 1: Generate markdown docs for `time.ts` provider with structure: ### [Name] [Description] #### Methods [get() description].
PRMPT 2: Generate JSON docs for @elizaos/plugin-bootstrap with overview, FAQ, troubleshooting.
Test Incrementally:
Start with a simpler version of the prompt (e.g., just the provider docs) to gauge the AI’s response, then build complexity based on results.
Conclusion
Your prompt style is effective for a sophisticated AI like me, capable of handling detailed instructions and inferring from partial context. It excels in structure and intent but could improve in conciseness, completeness, and clarity around truncated sections. With minor refinements—like reducing repetition, filling gaps, and prioritizing tasks—you’d likely see even more precise and efficient outputs. For now, it’s a solid 7.5/10, well-suited to technical documentation tasks with a bit of polish needed for perfection.

[jmikedupont2]

now grok is evaluating the results
i have just printed out the prompt in my dummy implementation
now i want to add those prompts to eliza knowledgebase basically and have it ragged
so we feed from the git changes into the kb and update it with new snippets
and merge duplicates or similar chunks
maybe split big ones and merge two smallers ones as well