LLM Prompting & Learning Guide

Friday. July 18, 2025 - 3 mins

🧠 LLM Prompting & Learning Guide

I spent today downloading and processing all of my chats with LLMs, with the purpose of compressing my previous prompts and using them to get an LLM prompt-review! I got some useful tips which I think might prove helpful for other researchers, then I’m going ahead and putting up this practical companion for improving efficiency, learning, and collaboration with language models.

📈 Strengths in LLM Interactions

1. Precision in Technical Queries

Clear and concise code-related queries.
Specific task-driven requests (e.g., “make this function a one-liner”).

2. Depth of Exploration

Engage conceptual boundaries (e.g., AI agency).
Stress-test LLM capabilities (e.g., “solve Riemann’s hypothesis”).

Follow up to clarify or correct model outputs.
Request simplifications, optimizations, or alternative approaches.

4. Multimodal Use

Apply LLMs across disciplines: code, music, philosophy, bureaucracy, etc.

⚙️ Areas for Improvement

1. Add Context to Abstract Queries

✅ Fix: Add background.
Example:

“In Transformer architectures, why must Q/K matrices share dimensionality? Explain with attention-score normalization.”

2. Avoid Over-Fragmentation

✅ Fix: Batch related ideas.
Example:

“Implement | and |= for my AronsonSet class. Ensure iter_dict updates.”

3. Clarify Ownership of Requests

✅ Fix: State whether you want critique, explanation, or rewrite.
Example:

“Critique this ceil() implementation for edge cases.”

4. Ask for Error Traps

✅ Fix: Prompt for edge cases and what can break.
Example:

“What could break this regex? r'\bT is the (\S+) letter'”

5. Balance Theory with Practice

✅ Fix: Anchor the theoretical with application or testing.
Example:

“Given Borges’ Tlön, design a test for LLM ‘independent thought’. Provide pseudocode.”

🧠 Interaction Style Reflection

Tone: Direct, efficient.
Strength: Low fluff, high information density.
Risk: Might discourage nuance.

✅ Suggested Style Enhancers:

“Explain like I’m a beginner.”
“Now optimize for brevity.”

🚀 Optimizing LLM Outputs

1. Pre-constrain Format

“Give a 3-sentence summary, then 3 bullet points of caveats.”

2. Force Prioritization

“Rank these by memory usage for n=1e6.”

3. Meta-Awareness

“What key question did I forget to ask about [topic]?”

🧭 The BQH Prompt Framework

Background: Give 1 sentence of context.
Question: What do you want?
Hypothesis: Share your assumption (optional).
Example:

“SIFT isn’t differentiable — confirm?”

📚 Learning and Development Tips

🛠️ For Programming:

Automate documentation (docstrings, linters).
Use test generation (LLM-assisted or libraries).
Read advanced PyTorch tutorials and profiling tools (e.g., Nsight).

🔬 For Theoretical Growth:

Dive deeper into <insert fields here>.
Study foundational <insert fields here>.

🧑‍🔬 Research Practices:

Use reproducibility tools (e.g., DVC).
Contribute to open-source CV/ML libraries.
Turn experiments into papers or interactive demos.

🤖 Using LLMs as a Learning Partner

Ask for failure cases:

“What edge cases should I test for this motion vector code?”
Simulate peer review:

“Critique this research idea. What assumptions might be invalid?”
Scaffold interaction:

“First explain grid_sample, then show how to use it with motion maps.”

🪄 Final Motto (kitsch alert)

“Optimize, but leave room for the serendipitous.”