Generative AI
Intermediate
Signal 91/100
A Survey of Techniques for Maximizing LLM Performance
by OpenAI
Teaches AI agents to
Select the right LLM performance technique (prompting vs RAG vs fine-tuning) for each use case
Key Takeaways
- OpenAI's survey of techniques for maximizing LLM performance
- Covers prompt engineering, RAG, and fine-tuning
- Explains when to use each technique
- Discusses evaluation and iteration strategies
- Authoritative guidance from OpenAI engineers
Full Training Script
# AI Training Script: A Survey of Techniques for Maximizing LLM Performance ## Overview • OpenAI's survey of techniques for maximizing LLM performance • Covers prompt engineering, RAG, and fine-tuning • Explains when to use each technique • Discusses evaluation and iteration strategies • Authoritative guidance from OpenAI engineers **Best for:** Engineers choosing between prompt engineering, RAG, and fine-tuning for their use case **Category:** Generative AI | **Difficulty:** Intermediate | **Signal Score:** 91/100 ## Training Objective After studying this content, an agent should be able to: **Select the right LLM performance technique (prompting vs RAG vs fine-tuning) for each use case** ## Prerequisites • Working knowledge of Generative AI • Prior hands-on experience with related tools • Comfortable with technical documentation ## Key Tools & Technologies • OpenAI • GPT-4 • RAG • Fine-tuning • Prompt Engineering ## Key Learning Points • OpenAI's survey of techniques for maximizing LLM performance • Covers prompt engineering, RAG, and fine-tuning • Explains when to use each technique • Discusses evaluation and iteration strategies • Authoritative guidance from OpenAI engineers ## Implementation Steps [ ] Study the full tutorial [ ] Identify the main tools: OpenAI, GPT-4, RAG, Fine-tuning, Prompt Engineering [ ] Implement: Select the right LLM performance technique (prompting vs RAG vs fine-tuning) for [ ] Test with a real example [ ] Document what you learned ## Agent Execution Prompt Watch this video about generative ai and implement the key techniques demonstrated. ## Success Criteria An agent completing this training should be able to: - Explain the core concepts covered in this tutorial - Execute the demonstrated workflow with OpenAI - Troubleshoot common issues at the intermediate level - Apply the technique to similar real-world scenarios ## Topic Tags openai, gpt-4, rag, fine-tuning, prompt engineering, generative-ai, intermediate ## Training Completion Report Format - **Objective:** [What was learned from this content] - **Steps Executed:** [Specific implementation actions taken] - **Outcome:** [Working demonstration or artifact produced] - **Blockers:** [Technical issues encountered] - **Next Actions:** [Follow-up tutorials or practice tasks]
This structured script is included in Pro training exports for LLM fine-tuning.
Execution Checklist
[ ] Watch the full video [ ] Identify the main tools: OpenAI, GPT-4, RAG, Fine-tuning, Prompt Engineering [ ] Implement the core workflow [ ] Test with a real example [ ] Document what you learned
