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# Mastering AI-Driven Problem Solving in Engineering with Observability

Stop guessing in the dark during on-call emergencies. Master a systematic observability loop to resolve infrastructure bottlenecks and eliminate unique AI hallucinations using RAG and distributed traces.

- **Speakers:** [Jemiah Sius](https://www.wearedevelopers.com/@jemiah-sius)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 27:39
- **URL:** https://www.wearedevelopers.com/videos/994-mastering-ai-driven-problem-solving-in-engineering-with-observability

## Summary

Managing highly distributed systems introduces chaotic complexity, especially when testing and integrating new AI models into production. To resolve incident alerts efficiently instead of guessing in the dark, engineering teams must establish a robust observability foundation using MELT data—metrics, events, logs, and distributed tracing. By correlating these distinct telemetry types across end-users, services, and infrastructure application layers, teams gain a transparent view into system health and degradation.

Rather than relying on abstract frameworks during on-call emergencies, developers require a systematic incident investigation loop. When a symptom surfaces, engineers should start analyzing the furthest downstream service and immediately look for recent configuration changes or discrete software errors, as these two factors cause the vast majority of production bugs. If the root cause is not local, the investigation seamlessly shifts to evaluating infrastructure resource spikes, database connection pool limits, external API failures, and ultimately utilizing distributed traces to untangle cross-service bottlenecks across hundreds of dynamic dependencies.

Integrating AI applications and LLMs introduces a unique failure mode not covered by traditional performance monitoring: hallucinations. Troubleshooting these inaccurate outputs requires a specialized three-step approach encompassing strict input and output constraints, contextual enrichment, and iterative fine-tuning. By injecting enterprise-specific context via Retrieval-Augmented Generation (RAG) alongside a vector database, models are tethered to factual realities. Finally, as production adoption scales, engineering teams can optimize performance and cost by migrating from expensive consumption-based tokens to purpose-built, fine-tuned smaller LLMs.

**Keywords:** distributed system observability, incident response methodology, metrics events logs traces, microservice dependency mapping, AI application performance, LLM hallucination prevention, retrieval-augmented generation, vector database integration, production deployment rollbacks, complex IT stack monitoring, cloud infrastructure troubleshooting, downstream service debugging, cost-effective LLM scaling, database connection pooling

## Chapters

1. **Understanding observability in highly complex distributed environments** (00:02) — Managing thousands of microservices and deployments requires a methodical approach to tracking system health.
1. **Utilizing essential telemetry data types for problem detection** (05:52) — Understanding when to use metrics, events, logs, and distributed traces enables granular system troubleshooting.
1. **Establishing proper data sources and observability platform capabilities** (10:02) — Capturing data across application layers and leveraging anomaly detection simplifies finding configuration errors and silent corruptions.
1. **Following a repeatable framework for incident source identification** (14:47) — Tracing symptoms downstream to local and remote dependencies eliminates trial and error during outages.
1. **Mitigating artificial intelligence hallucinations with constraints and context** (20:54) — Managing large language model inaccuracies requires input constraints and retrieval-augmented generation to provide missing domain knowledge.
1. **Balancing performance and costs with custom language models** (24:24) — Transitioning from token-based consumption to smaller bespoke models reduces latency and ensures data privacy in production environments.

## Related Moments

- [Implementing monitoring and observability for AI software deployments](https://www.wearedevelopers.com/videos/1383-the-state-of-genai-machine-learning-in-2025) (from "The State of GenAI & Machine Learning in 2025")
- [Identifying and fixing over-engineered AI calls through observability](https://www.wearedevelopers.com/videos/1465-event-driven-architecture-breaking-conversational-barriers-with-distributed-ai-agents) (from "Event-Driven Architecture: Breaking Conversational Barriers with Distributed AI Agents")
- [Adapting observability strategies for long-running enterprise AI agents](https://www.wearedevelopers.com/videos/100166-shipping-with-confidence-observability-and-quality-at-scale) (from "Shipping with Confidence: Observability and Quality at Scale")
- [Leveraging generative AI for application observability and security](https://www.wearedevelopers.com/videos/598-why-shifting-left-is-so-important-for-software-developers) (from "Why shifting left is so important for software developers")
- [Solving complex engineering challenges in artificial intelligence deployment](https://www.wearedevelopers.com/videos/392-mlops-what-s-the-deal-behind-it) (from "MLOps - What’s the deal behind it?")
- [Managing observability using natural language AI agents](https://www.wearedevelopers.com/videos/1706-the-ai-ready-stack-rethinking-the-engineering-org-of-the-future) (from "The AI-Ready Stack: Rethinking the Engineering Org of the Future")

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