Fix Bad Outputs Without Starting Over

Fix bad AI outputs with this practical ChatGPT debugging guide and prompt engineering tips. Learn how to fix AI mistakes, debug prompts, and improve ChatGPT responses without starting over. Walk away with a simple step‑by‑step system to troubleshoot AI prompts and get better generative AI results fast. What You'll Learn: • A simple 3-part framework to diagnose why ChatGPT (or any LLM) gave you a bad answer. • How to quickly spot whether the problem is unclear instructions, missing context, or poorly framed constraints. • Specific follow-up prompts to clarify and re-order instructions for cleaner, more relevant outputs. • How to inject missing knowledge, data, and examples so the AI stops guessing and starts following your reality. • Tactical ways to tighten or relax constraints to control style, length, and format of generative AI results. • How to use short, surgical follow-up prompts to recover 70–90% of what you wanted—without rewriting from scratch. • A simple habit for writing down key insights, identifying one real-world use case, and taking one tiny action this week. Episode Content: 00:00 - Why “bad AI outputs” usually aren’t the model’s fault 04:02 - The 3 root causes of bad generative AI results 09:15 - Debugging step 1: Fixing unclear or tangled instructions 15:48 - Debugging step 2: Supplying missing context and concrete examples 22:31 - Debugging step 3: Adjusting constraints for style, tone, and length 29:05 - Using follow-up prompts instead of starting over from scratch 34:40 - Common misconceptions about prompt engineering and AI “hallucinations” 41:10 - How to turn this episode into action: write, apply, and take one small step

What You'll Learn:

• A simple 3-part diagnostic to classify any bad AI output as an instruction, context, or constraint problem.
• How to rewrite or re-order your instructions so ChatGPT understands your real goal and steps.
• Practical ways to add missing context (data, constraints, audience, examples) so the model stops guessing.
• How to tighten or relax constraints to control tone, level of detail, and output format for better LLM quality.
• Concrete follow-up prompt templates you can use to fix AI mistakes without restarting the entire conversation.
• A lightweight workflow for troubleshooting AI prompts in under 5 minutes instead of endlessly rewriting them.
• How to capture the key ideas from this episode, map them to one real situation, and take a single small action this week.

Episode Content:

• 00:00 - Why “bad AI outputs” usually aren’t the model’s fault
• 04:02 - The 3 root causes of bad generative AI results
• 09:15 - Debugging step 1: Fixing unclear or tangled instructions
• 15:48 - Debugging step 2: Supplying missing context and concrete examples
• 22:31 - Debugging step 3: Adjusting constraints for style, tone, and length
• 29:05 - Using follow-up prompts instead of starting over from scratch
• 34:40 - Common misconceptions about prompt engineering and AI “hallucinations”
• 41:10 - How to turn this episode into action: write, apply, and take one small step