There’s been an explosion of AI prototypes thanks to the rise of vibe coding tools, but the gap between prototype and production, between what AI can help you start but can’t help finish has become the limiter for founders and product builders.
Common issues and failure modes when taking something into production.
Massive growth in prototyping, but few product launches to match.
Top 10 reasons why projects fail to reach production (including issues to contend with for an AI-native product).
The market is now more skeptical of AI, and AI spending is plateauing as the hype dies down.
Summary of the current AI environment we are operating in.
What we are covering today are the 3 main aspects of bridging the gap between Prototype and Production. Firstly, fundamentals that apply to any software product development. Secondly, considerations unique to developing AI Native features or products. And thirdly, how to use AI in the development process itself for both AI and non-AI features.
The objective of the AI-Assist Feature was to improve the training process, serve thousands of users, and earn trust despite using probabilistic AI for a a gamified training app (1Huddle) for frontline workers..
The basic, early architecture of the prototype.
The significantly complex, enterprise-ready architecture that includes RAG, LangChain agents, security, and observability tools.
Security Architecture Diagram: Visualizing the secure setup: LLM inference (AWS Bedrock) placed inside the private cloud (VPC) with restricted API access.
A breakdown of all costs beyond just inference: governance, memory layers (Vector DBs), embeddings, and I/O charges.
The six universal requirements for durable software: Security, Observability, Stability, Reliability, Cost Predictability, and Operability.
Security: Security Principle of Zero Privileges. New AI concerns requiring prompt-filtering Guardrails, manual review, and isolated component communication.
Observability: Essential for providing reliable, consistent behavior; requires logs, metrics, and specific metrics.
Stability: Building infrastructure that can scale and handle failures.
Reliability: Achieving reliability through comprehensive testing; an area where AI can provide significant leverage.
Cost Predictability: The necessity of an optimization mindset to manage high LLM inference costs and control the budget.Type image caption here (optional)
Operability: The safety-first approach: Soft Launch (Blue/Green) with continuous validation using the DeepEval/DP-val framework. Result of this process is stability, predictability, cost effectiveness, and, most importantly, trust.
The AI Coding Dilemma: AI amplifies expertise, but also bad coding practices, making debugging harder and pushing the bottleneck to code review.
Human Ownership: The necessity of manual code review and human ownership; cannot allow AI to review AI. Spec-Driven Development, small steps, and highly controlled use of AI to ensure review ability and sustainability.
Seven Guiding Principles: Reduce unknowns, minimize privileges, separate reasoning from action, observe before optimizing, cap cost, and Make AI Boring Before Ambitious.
The full seven-step process: Identify failures, build observability, add controls, constrain privileges, cap compute, human oversight, and staged rollout.
What to Take Forward
AI tools are a game changer for startups and product teams. At Codewalla, we help teams turn prototypes into production grade solutions, AI or not, that can survive the demands of actual customers.
If the slides spark questions or highlight challenges in your own prototype-to-production path, reach out.
