Building LLM applications for engineers across industries requires more than just technical prowess—it demands deep understanding of workflows, trust, and integration into real-world environments. Over the past two years, I’ve worked closely with domain experts such as process engineers, reliability engineers, and cybersecurity analysts who rely on logs, schematics, and reports for tasks like troubleshooting, failure analysis, and compliance checks. The promise of LLMs is compelling: leverage vast pre-trained knowledge to automate tedious pattern-matching tasks and free experts for higher-level decisions. But in practice, turning a flashy demo into a trusted tool is far more complex. Here are ten lessons learned from building these systems, organized into three phases: before, during, and after development. Before You Start Lesson 1: Not every problem needs an LLM. Ask whether rule-based logic, analytical models, or classic ML could solve 80% of the issue. LLMs shine when dealing with unstructured text, synthesis, or reasoning across messy artifacts—but they’re costly, slow, and unpredictable. If you need precise, reproducible results, keep the core logic in deterministic systems. If there’s no human in the loop, LLMs are risky. If your use case involves high volume and low latency, LLMs may not scale. Lesson 2: Frame the tool as augmentation, not automation. Position the LLM as a helper that speeds up triage, analysis, and exploration—never as a replacement. This mindset reduces resistance from engineers and makes mistakes easier to discuss constructively. When the tool fails, the conversation becomes “this suggestion wasn’t perfect but gave me new ideas,” not “your AI failed.” Lesson 3: Co-design with experts and define “better.” Involve domain experts early to understand their real pain points, tools, and workflows. Define success metrics in their language—like reduced triage time, fewer false leads, or fewer manual steps. This shared definition becomes your benchmark and builds trust through collaboration. During The Project Lesson 4: Build a co-pilot, not an auto-pilot. Engineers need control and visibility. Instead of auto-classifying all alarms, design systems that group them, show reasoning, and let experts approve or adjust. This transparency builds trust and makes the tool feel like a partner, not a black box. Lesson 5: Focus on workflow, roles, and data flow before choosing a framework. Don’t start with LangGraph or AutoGen. Use simple SDK calls and basic control flow to test core assumptions quickly. Once the workflow works, migration to a production framework is easier. Keep it lean and fast. Lesson 6: Start with deterministic workflows, not agents. Most engineering tasks follow known patterns. Encode that domain knowledge into structured, step-by-step workflows. They’re more reliable, explainable, and easier to debug than agentic systems. Only introduce agent-like behavior if the workflow hits a limit. Lesson 7: Structure everything—inputs, outputs, and knowledge. Feed LLMs clean, structured data like JSON parsed from logs or reports. Use structured output formats (e.g., Pydantic models) so the LLM returns consistent, machine-readable results. This enables better retrieval in RAG, easier debugging, and reliable citations. Lesson 8: Combine analytical AI with generative AI. Use classic ML for pattern detection, anomaly detection, or clustering—tasks where speed, precision, and determinism matter. Then layer LLMs on top to explain, summarize, or recommend next steps. This hybrid approach leverages the strengths of both. After You Build Lesson 9: Integrate where engineers already work. A standalone web app or chat interface rarely gets adopted. Embed LLM features directly into existing tools—like adding a “summarize” button in a log viewer or a “suggest next steps” panel in a ticketing system. Use task-specific actions, not open-ended chat. Pass context dynamically—like the current incident ID or time window—so engineers don’t have to re-enter it. Lesson 10: Evaluate relentlessly with real cases. Show your system’s work: what it saw, what steps it took, and how confident it is. Encourage LLMs to cite evidence and assign confidence levels. Then run evaluation sessions with experts on real historical cases. Ask them to think aloud: Is this accurate? Are the suggestions reasonable? What’s missing? Use these insights to refine the system. In conclusion, success comes from respecting domain expertise, engineering the system with clarity and structure, and treating deployment as the start of real work—not the end. The best LLM tools don’t replace engineers—they amplify them.