Avoiding Busywork With LLM Tools

I used this guide to build 10+ AI Agents Here're my 10 actionable items: 1. Turn your agent into a note-taking machine → Dump plans, decisions, and results into state objects outside the context window → Use scratchpad files or runtime state that persists during sessions → Stop cramming everything into messages - treat state like external storage 2. Be ridiculously picky about what gets into context → Use embeddings to grab only memories that matter for current tasks → Keep simple rules files (like CLAUDE md) that always load → Filter tool descriptions with RAG so agents aren't confused by irrelevant tools 3. Build a memory system that remembers useful stuff → Create semantic, episodic, and procedural memory buckets for facts, experiences, instructions → Use knowledge graphs when embeddings fail for relationship-based retrieval → Avoid ChatGPT's mistake of pulling random location data into unrelated requests 4. Compress like your context window costs $1000 per token → Set auto-summarization at 95% context capacity with no exceptions → Trim old messages with simple heuristics: keep recent, dump middle → Post-process heavy tool outputs immediately - search results don't live forever 5. Split your agent into specialized mini-agents → Give each sub-agent one job and its own isolated context window → Hand off context with quick summaries, not full message histories → Run sub-agents in parallel when possible for isolated exploration 6. Sandbox the heavy stuff away from your LLM → Execute code in environments that isolate objects from context → Store images, files, complex data outside the context window → Only pull summary info back - full objects stay in sandbox 7. Make summarization smart, not just chronological → Train models specifically for agent context compression → Preserve critical decision points while compressing routine chatter → Use different strategies for conversations vs tool outputs 8. Prune context like you're editing a novel → Implement trained pruners that understand relevance, not just recency → Filter based on task relevance while maintaining conversational flow → Adjust pruning aggressiveness based on task complexity 9. Monitor token usage like a hawk → Track exactly where tokens burn in your agent pipeline → Set real-time alerts when context utilization hits dangerous levels → Build dashboards correlating context management with success rates 10. Test everything or admit you're just guessing → A/B test different context strategies and measure performance differences → Create evaluation frameworks testing before/after context engineering changes → Set up continuous feedback loops auto-adjusting context parameters Last but not the least, be open to new ideas and keep learning Check out 50+ AI Agent Tutorials on my profile 👋 .

Don't ask an LLM to do your evals. Instead, use it to accelerate them. LLMs can speed up parts of your eval workflow, but they can’t replace human judgment where your expertise is essential. Here are some areas where LLMs can help: 1. First-pass axial coding: After you’ve open coded 30–50 traces yourself, use an LLM to organize your raw failure notes into proposed groupings. This helps you quickly spot patterns, but always review and refine the clusters yourself. Note: If you aren’t familiar with axial and open coding, see this faq: https://lnkd.in/gpgDgjpz 2. Mapping annotations to failure modes: Once you’ve defined failure categories, you can ask an LLM to suggest which categories apply to each new trace (e.g., “Given this annotation: [open_annotation] and these failure modes: [list_of_failure_modes], which apply?”). 3. Suggesting prompt improvements: When you notice recurring problems, have the LLM propose concrete changes to your prompts. Review these suggestions before adopting any changes. 4. Analyzing annotation data: Use LLMs or AI-powered notebooks to find patterns in your labels, such as “reports of lag increase 3x during peak usage hours” or “slow response times are mostly reported from users on mobile devices.” However, you shouldn’t outsource these activities to an LLM: 1. Initial open coding: Always read through the raw traces yourself at the start. This is how you discover new types of failures, understand user pain points, and build intuition about your data. Never skip this or delegate it. 2. Validating failure taxonomies: LLM-generated groupings need your review. For example, an LLM might group both “app crashes after login” and “login takes too long” under a single “login issues” category, even though one is a stability problem and the other is a performance problem. Without your intervention, you’d miss that these issues require different fixes. 3. Ground truth labeling: For any data used for testing/validating LLM-as-Judge evaluators, hand-validate each label. LLMs can make mistakes that lead to unreliable benchmarks. 4. Root cause analysis: LLMs may point out obvious issues, but only human review will catch patterns like errors that occur in specific workflows or edge cases—such as bugs that happen only when users paste data from Excel. Start by examining data manually to understand what’s going wrong. Use LLMs to scale what you’ve learned, not to avoid looking at data. Read this and other eval tips here: https://lnkd.in/gfUWAjR3

Just had a major realization that's changing how I work with AI tools. We've all heard "𝐦𝐨𝐫𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 = 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐧𝐬𝐰𝐞𝐫𝐬" but I'm finding the opposite can be true! I've seen it firsthand - feed an LLM too much information without proper management and you get what I call "Context Distraction." The AI becomes overwhelmed, fixates on irrelevant details, and starts repeating itself instead of generating fresh insights. It's like trying to have a productive conversation with someone who's reading through a 200-page transcript of everything you've ever discussed. At some point, focus gets lost. Two approaches that have dramatically improved my results: 1️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐒𝐮𝐦𝐦𝐚𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Instead of dumping entire conversation histories into new prompts, I summarize key points and decisions. This gives the AI a clean slate with just the essential context. 2️⃣ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐎𝐟𝐟𝐥𝐨𝐚𝐝𝐢𝐧𝐠: Breaking complex projects into discrete conversations rather than one massive thread. I keep track externally (basic notes work fine) and only introduce relevant information when needed. The difference in output quality is remarkable. My conversations are more focused, responses are more creative, and I'm getting better solutions faster. Who else has noticed this pattern? Any other techniques you've found effective for managing AI context? #ArtificialIntelligence #LLMs #ProductivityHacks #AITools

As you build your next agent or optimize an existing one, ask yourself: Is everything in this context earning its keep? If not... Here are six ways to fix it: As we learned in the research paper, “Lost in the middle”, LLMs don't treat every token in their context window equally. Across 18 models (GPT-4, Claude, Gemini, etc.), performance degrades as input length grows in surprising ways. Four key failure modes have been put into the spotlight: • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗼𝗶𝘀𝗼𝗻𝗶𝗻𝗴 - Errors that get repeatedly referenced • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗗𝗶𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 - Models focus on history instead of training • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗖𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻 - Too much content influences quality • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗖𝗹𝗮𝘀𝗵 - Conflicting information degrades reasoning Here are 6 proven techniques to fix these issues: 1️⃣ 𝗥𝗔𝗚 - Selectively add only relevant information 2️⃣ 𝗧𝗼𝗼𝗹 𝗟𝗼𝗮𝗱𝗼𝘂𝘁 - Choose only relevant tools for your context 3️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗤𝘂𝗮𝗿𝗮𝗻𝘁𝗶𝗻𝗲 - Isolate contexts in dedicated threads 4️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝘂𝗻𝗶𝗻𝗴 - Remove irrelevant information 5️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - Condense verbose content 6️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗢𝗳𝗳𝗹𝗼𝗮𝗱𝗶𝗻𝗴 - Store information outside LLM context