You are tasked with evaluating the effectiveness of different AI prompt strategies for extracting valuable information from a large archive of diverse assets (documents, images, audio, video).  The archive contains materials from various sources and formats, some well-organized and some highly disorganized.  Your goal is to identify the best prompt engineering techniques for extracting specific information, such as contact details, dates, project summaries, key findings, or visual elements.  </p>
<p>**Evaluation Criteria:**  Assess each prompt strategy based on its precision (accuracy of extracted information), recall (completeness of information found), efficiency (time taken to process a given amount of data), and scalability (ability to handle large datasets). Consider factors like prompt length, complexity, and use of parameters.</p>
<p>**Prompt Strategies to Evaluate:**</p>
<p>1. **Keyword-based prompts:**  Simple prompts using keywords to identify relevant assets.<br />
2. **Contextual prompts:** Prompts that provide background information and context to improve accuracy.<br />
3. **Multi-modal prompts:** Prompts that incorporate multiple data types (text, images, etc.) to extract information.<br />
4. **Fine-tuned model prompts:** Prompts designed for a specific model fine-tuned on a subset of the archive.<br />
5. **Iterative refinement prompts:** Prompts that are iteratively refined based on initial results to improve accuracy.</p>
<p>**Output Format:** For each prompt strategy, provide a table summarizing the performance metrics (precision, recall, efficiency, scalability) along with qualitative observations on its strengths and weaknesses.  Include specific examples of prompts used and the results obtained.  Finally, provide a recommendation for the most effective overall strategy based on your evaluation.  The evaluation should consider the trade-offs between accuracy, efficiency, and scalability.