The Fifth Elephant
The Fifth Elephant

The Fifth Elephant Pune edition

Call for Proposals

All submissions

Govind Joshi

@govindkrjoshi1

Engineering maturity is all you need

Submitted Dec 9, 2025

Indicate the track in which your submission fits: Track 2: AI in Manufacturing & Digital Transformation Type of submission: 30 mins talk

This talk starts with a familiar story: the 8:30 PM scramble before a demo, endlessly tweaking prompts until the bot behaves just enough for tomorrow morning. The demo goes well, everyone is happy, the feature is greenlit—and then it quietly falls apart in production. Users repeat themselves. Interruptions break the flow. Tool calls misfire. You have recordings but no traces, complaints but no repro steps, and you’re stuck in the same “tweak and pray” loop—just with more traffic and higher stakes.

In this session, I’ll argue that the difference between “cool demo” and “reliable product” is not model choice or prompt cleverness, but engineering maturity: documentation, observability, evals, datasets, CI/CD, and feedback loops. We’ll reframe AI product development as discovery, not invention and walk through concrete practices for building that discovery engine: how to log and trace every LLM and tool call, design evals that actually catch regressions, turn production traffic into datasets, and build a flywheel where every failure makes the system stronger. You’ll leave with a pragmatic checklist you can apply to your current AI project without a full platform rewrite.

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Mention 1–2 takeaways from your session
• You’ll learn a practical definition of engineering maturity for AI applications and a minimal set of non-negotiables (docs, observability, evals, datasets, CI/CD) that turn fragile demos into reliable systems.
• You’ll leave with a concrete “flywheel” pattern for AI products—how to capture data, tag outcomes, run evals, and iterate—so you can answer “Can we ship this to 100,000 users?” with data instead of hope.

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Which audiences is your session going to be beneficial for?

This session will be most useful for:
• Engineering managers and tech leads responsible for shipping AI features to production
• Senior/principal engineers and ML/AI engineers working with LLMs, tools, and agents
• Product managers and founders trying to turn promising AI prototypes into reliable products
• Platform / infra / DevOps engineers designing internal AI platforms or evaluation/observability stacks

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Add your bio – who you are; where you work

I’m Govind Joshi, an independent software engineer based in India who spends an unreasonable amount of time building AI-powered systems that actually have to work in the real world. I focus on applied AI: LLM-driven agents that can call tools, handle real users over phone and chat, and operate reliably under production traffic.

Over the last few years, I’ve worked with teams to design and ship AI assistants, voice bots, and evaluation/observability pipelines for LLM applications. I care a lot about the “boring” parts—architecture, evals, monitoring, and engineering maturity—and how they turn AI demos into products you can trust.

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