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Some useful prompting tips here.

In-context-learning is always the best.

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Examples beat instructions Wasted weeks writing perfect instructions. Then tried 3-4 examples and got instant results. Models pattern-match better than they follow rules (except reasoning models like o1)

Version control your prompts like code One word change broke our entire system. Now I git commit prompts, run regression tests, track performance metrics. Treat prompts as production code

Test coverage matters more than prompt quality Built a test suite with 100+ edge cases. Found my "perfect" prompt failed 30% of the time. Now use automated evaluation with human-in-the-loop validation

Domain expertise > prompt tricks Your medical AI needs doctors writing prompts, not engineers. Subject matter experts catch nuances that destroy generic prompts

Temperature tuning is underrated Everyone obsesses over prompts. Meanwhile adjusting temperature from 0.7 to 0.3 fixed our consistency issues instantly

Model-specific optimization required GPT-4o prompt ≠ Claude prompt ≠ Llama prompt. Each model has quirks. What makes GPT sing makes Claude hallucinate

Chain-of-thought isn't always better Complex reasoning chains often perform worse than direct instructions. Start simple, add complexity only when metrics improve

Use AI to write prompts for AI Meta but effective: Claude writes better Claude prompts than I do. Let models optimize their own instructions

System prompts are your foundation 90% of issues come from weak system prompts. Nail this before touching user prompts

Prompt injection defense from day one Every production prompt needs injection testing. One clever user input shouldn't break your entire system

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From r/PromptEngineering

New @GoogleResearch paper builds a personal health assistant that reads a user’s data, answers health questions, and coaches daily habits.

It evaluates the system on 10 tasks with 7,000+ human annotations and 1,100 hours from experts and users.

The assistant covers 4 needs, interpreting a user’s data, giving clear facts, offering wellness advice, and handling symptom questions.

It uses 3 specialist agents, a data science agent, a domain expert agent, and a health coach agent.

The data science agent plans analyses and writes code to turn wearable streams and medical records into clear numbers.

The domain expert agent joins those numbers with medical knowledge and user context to explain findings and reason about likely causes.

The health coach agent runs multi turn chats that set goals, surface barriers, and track progress using motivational interviewing.

An orchestrator decides which agent to call, coordinates their work, and returns one joined answer.

In studies the agents and the full system improved diagnosis quality and increased user rated coaching helpfulness versus a strong baseline.

Overall, this is a practical template for turning a general chatbot into a personal health assistant grounded in personal data.

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Paper – arxiv. org/abs/2508.20148

Paper Title: "The Anatomy of a Personal Health Agent"

Can LLM agents can trade stocks profitably in a realistic, clean setup.

The paper tests that and builds StockBench, a clean setup where an agent reads daily prices, fundamentals, and news, then chooses buy, sell, or hold.

They show that these agents can sometimes make profits, but their performance is inconsistent and often no better than a basic equal-weight buy-and-hold portfolio.

The window uses fresh 2025 data to block training leakage.

The stock pool is 20 large Dow Jones names for stable inputs.

Each morning the agent reviews positions, pulls extra fundamentals on chosen stocks, then sets dollar targets that turn into shares.

Results use final return, maximum drawdown, and the Sortino ratio for downside risk.

Across 82 days some agents beat equal weight buy and hold and reduce losses, but gains are small.

Reasoning tuned models do not consistently win, larger portfolios add noise, and removing news or fundamentals hurts results.

So LLM agents can trade and manage risk to a degree, yet strong and reliable outperformance is not shown.