AI Tips

Master the art of prompt engineering with these techniques. Write prompts that get better results from AI models. Top 10 Techniques 1. Be specific and clear 2. Provide context and background 3. Use examples (few-shot) 4. Break down complex tasks 5. Specify output format 6. Set constraints and rules 7. Use system prompts 8. Iterate … Read more

Set and manage token budgets. Control costs in production applications. Budgeting Strategies 1. Set per-request limits 2. Track usage per user 3. Implement quotas 4. Alert on thresholds Implementation class TokenBudget: def __init__(self, max_tokens=10000): self.max_tokens = max_tokens self.used = 0 def check(self, tokens): return self.used + tokens

Process multiple requests efficiently. Batch requests to reduce overhead. Batching Strategies 1. Combine similar requests 2. Use batch endpoints 3. Parallel processing Code Example prompts = [“prompt1”, “prompt2”, “prompt3”] batch_response = process_batch(prompts) Conclusion Batching improves efficiency!

Implement caching to reduce API calls. Cache responses for efficiency and cost savings. Caching Types ✅ Response caching ✅ Embedding caching ✅ Semantic caching Implementation from functools import lru_cache @lru_cache(maxsize=1000) def get_response(prompt): return client.chat.completions.create(…) Conclusion Caching reduces redundant API calls!

Compress prompts without losing quality. Reduce token count while maintaining effectiveness. Techniques 1. Remove filler words 2. Use abbreviations 3. Combine instructions 4. Reference instead of repeat Example Before: “Please write a comprehensive article about…” After: “Write article about…” Conclusion Compression maintains quality while saving tokens!

Practical strategies to reduce token usage. Save money on API costs with these techniques. Strategies 1. Prompt compression 2. Remove redundancy 3. Use shorter examples 4. Optimize system prompts Before/After Before: 1000 tokens After: 400 tokens (60% reduction) Conclusion Token optimization significantly reduces costs!

Manage memory in OpenClaw for context-aware responses. Build assistants that remember and learn. Memory Types ✅ Short-term memory ✅ Long-term memory ✅ Working memory Memory Files – MEMORY.md: Long-term memories – memory/YYYY-MM-DD.md: Daily notes Conclusion Memory enables context-aware assistants!

OpenClaw is a powerful AI assistant framework. Build custom AI assistants with OpenClaw. Features ✅ Multi-platform support ✅ Custom skills ✅ Memory management ✅ Tool integration Getting Started 1. Install OpenClaw 2. Configure your API keys 3. Create custom skills 4. Deploy your assistant Skills Extend OpenClaw with custom skills for your needs. Conclusion OpenClaw … Read more

Monitor and optimize API costs. Track spending and reduce costs. Monitoring Tools ✅ Built-in dashboards ✅ Custom tracking ✅ Alerts Cost Tracking class CostTracker: def __init__(self): self.total_tokens = 0 self.total_cost = 0 def track(self, input_tokens, output_tokens): self.total_tokens += input_tokens + output_tokens self.total_cost += calculate_cost(…) Conclusion Monitoring prevents bill surprises!

Choose between streaming and batch responses. Understand when to use each approach. Streaming ✅ Real-time output ✅ Better UX ✅ Early stopping Batch ✅ Simpler code ✅ Full response ✅ Easier testing When to Use Streaming: Chat apps, long responses Batch: Processing, batch jobs Conclusion Choose based on your use case!