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Randall Hunt, Chief Technology Officer at Caylent, is a technology leader, investor, and hands-on-keyboard coder based in Los Angeles, CA. Previously, Randall led software and developer relations teams at Facebook, SpaceX, AWS, MongoDB, and NASA. Randall spends most of his time listening to customers, building demos, writing blog posts, and mentoring junior engineers. Python and C++ are his favorite programming languages, but he begrudgingly admits that Javascript rules the world. Outside of work, Randall loves to read science fiction, advise startups, travel, and ski.
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Guille Ojeda is a Principal Innovation Architect at Caylent, a speaker, author, and content creator. He has published 2 books, over 200 blog articles, and writes a free newsletter called Simple AWS with more than 45,000 subscribers. He's spoken at multiple AWS Summits and other events, and was recognized as AWS Builder of the Year in 2025.
View Guille's articlesGenerative AI proofs of concept (POC) tend to work beautifully, with demos impressing stakeholders, accuracy looking solid, and everyone getting excited about shipping to production. Then reality hits. Latency spikes when traffic increases, costs spiral out of control, and users complain that the AI doesn't understand their actual workflows. At Caylent, we've seen this pattern play out over 200 times across enterprise deployments we've done for our customers, from Fortune 500 companies to ambitious startups building everything from intelligent document processing systems to multimodal search engines.
The gap between POC success and production failure often arises because most teams optimize the wrong things. They obsess over model selection, spend weeks fine-tuning when prompt engineering would suffice, and build elaborate tool-calling systems for operations that could be handled with three lines of Python. Meanwhile, the fundamentals that actually determine success in production get neglected: a clear specification of inputs and outputs, strategic context management, proper evaluation frameworks, and honest economic analysis.
This is part 1 of a 2-part series. In this part, we'll share the 6 key lessons we learned from deploying over 200 Generative AI applications to production. In part 2, we’ll share the specific patterns that work and the anti-patterns that consistently fail, drawn from our own experience building generative AI systems.
Before diving into technical patterns, it helps to understand where your application fits. We've found that generative AI projects generally fall into three categories, each with different success criteria and technical requirements:
Here's the fundamental question that determines whether your generative AI system has a defensible advantage: What are your inputs, and what are your outputs? This specification defines your business logic and value proposition. Everything else, your choice of models, your system architecture, even your vector database, is incidental. These components will evolve as better options become available, but your fundamental definition of what problem you're solving should remain stable.
Think about your system architecture in layers. At the foundation, you have your inputs and outputs. One layer up, you have evaluations that prove your system produces correct outputs for given inputs. This evaluation layer is crucial because it's what prevents regressions as you iterate. Without robust evals, you're just doing vibe checks and hoping things work. Above that sits your system architecture, and only at the top do you have the specific LLMs and tools you're using.
Most teams invert this pyramid. They spend weeks agonizing over whether to use Anthropic Claude or ChatGPT-5, trying to squeeze out an extra two points on some benchmark, while their input/output specification remains vague and their evaluation coverage is nonexistent. When we talk to customers about their struggling generative AI projects, the conversation often goes like this: "Tell us exactly what inputs your system receives and what outputs it should produce." Often, we’ll hear something like, "Well, users ask questions, and we want good answers." That's not a specification, it's a hope.
The evaluation layer deserves special emphasis here. Your evals are what separate production-ready systems from demos. When you first build something, you do a vibe check: you try a few prompts, see if the output looks reasonable, and call it good enough. That initial vibe check actually becomes your first eval. Then you change the data you're sending in, capture edge cases, and within twenty minutes of iteration, you have a working eval set. You don't need hundreds of examples initially. You just need enough to catch regressions when you change your prompts or upgrade models.
Embeddings sound like the context panacea: Convert everything to vectors, do semantic search, and suddenly your users can find anything with natural language queries. In practice, embeddings are necessary but insufficient for production search systems.
Consider a stock footage library with thousands of nature videos. Users don't just want semantically similar content; they likely need to filter by resolution, duration, licensing terms, and specific subjects. Pure vector search can't handle this, so the solution is a hybrid search that combines vector similarity with structured filters. In Postgres, you can query with WHERE clauses alongside vector similarity. In OpenSearch, you can combine semantic search with aggregations and filters. This architectural decision profoundly impacts user experience.
Understanding your access patterns drives these decisions. If users primarily browse categories and occasionally search within them, you need different indexes than if they start with freeform text and then apply filters. If faceted search (showing how many results exist in each category before users filter) is critical, you need aggregation capabilities that pure vector databases don't provide. These requirements should drive your database selection, not just comparing benchmark numbers on vector similarity recall.
The evaluation framework for search systems also differs from simple prompt-response pairs. You need to test across different query types: exact matches, semantic matches, filtered searches, and edge cases like typos or ambiguous terms. Boolean metrics often work better than complex scoring. For each test query, did the system return the expected documents in the top results? Yes or no. This clarity makes it easy to spot regressions when you change your embedding model or search parameters.
Large language model (LLM) inference is slower than traditional backend APIs. Even fast models take seconds for complex reasoning tasks, and users notice. The temptation is to obsess over inference speed, but UX orchestration and perceived latency matter more than raw performance. If you're slower than competitors but also more expensive, you're in trouble. However, if you're slower but cheaper, and you mitigate latency through thoughtful UX, you can still win.
One of our most successful patterns is generative UI, implemented for CloudZero's AWS cost analysis platform. Initially, we built a chatbot that allowed users to query their infrastructure costs and receive text responses. This worked, but felt limited. We evolved to generating UI components just-in-time from the query and data. When a user asks about cost trends, the system generates a React component with the appropriate chart type, renders it inline, and caches the component definition. Future queries can reference these cached components, creating an interface that personalizes and improves over time.
The technical approach involves generating the React component as part of the LLM response, then injecting it into the rendering pipeline. This adds latency because you're not just generating text; you're generating executable code. But users perceive this as valuable work happening, especially when they see a sophisticated visualization appear. The key insight is that perceived performance depends on whether users feel the system is making progress. A spinner on a blank screen feels like waiting. A streaming response that gradually reveals structure feels like work being done.
Other techniques that improve perceived performance include streaming responses with skeleton UI, optimistic rendering where you show predicted results immediately and update them when the real response arrives, and background pre-computation for predictable queries. The goal isn't to match traditional API latency; it's to make latency acceptable by providing visible progress and genuine value. Users will tolerate a three-second response if it delivers a customized analysis they couldn't get any other way. They won't tolerate a one-second response that provides generic information they could find faster with a traditional interface.
Understanding your users' actual environment and constraints often matters more than technical elegance. We learned this lesson repeatedly across different deployments.
A hospital system asked us to build a voice-based interface for nurses to update patient records hands-free. The logic seemed sound: nurses are often wearing gloves, holding instruments, or otherwise unable to type. Voice dictation would save time and improve workflow. We built a solid voice transcription system integrated with their records management platform, and it worked well in testing. In production, nurses hated it. Hospitals are loud environments with background noise from equipment, conversations, alarms, and overhead announcements. The voice transcription constantly picked up irrelevant audio, leading to errors and frustration. After observing actual usage, we switched to a traditional chat interface on mobile devices. Less innovative, but far more practical given the actual constraints.
Another deployment involved field technicians in remote areas with limited connectivity, sometimes on satellite internet with high latency and low bandwidth. We built a system that generated text summaries of maintenance documentation, then offered to send the full PDF manual. The summaries worked great, short text transferred quickly. But the PDFs were often 200MB, and downloads would time out or take forever. The solution was server-side PDF rendering. We'd take screenshots of just the relevant pages and send those images, which were typically a few megabytes instead of hundreds. Users gained context from the text summary and visual reference from the key pages without having to wait for massive downloads. The technical architecture became more complex, but it matched the actual usage constraints.
These examples illustrate a broader principle: context about your users' environment and workflows creates a competitive advantage. If you know they're in a noisy environment, provide visual interfaces. If you know they have bandwidth constraints, optimize data transfer. If you know they're typically on a specific page in your application when they invoke AI features, inject that page context into your prompts. Competitors who lack this contextual understanding will deliver inferior results even with identical models and embeddings. Your data advantage isn't just what you've embedded in a vector database; it's the real-time context you can provide about who the user is, what they're trying to do, and what constraints they're operating under.
One of the most common anti-patterns we see is teams building elaborate tool-calling systems for operations that could be handled with basic code. A classic example we encounter often is defining a tool called get_current_date. You control the prompt and can inject the current date as a string. There's no reasoning required, no ambiguity to resolve, just a deterministic computation.
This happens because teams misunderstand when to use LLM reasoning versus regular code. LLMs excel at reasoning, ambiguity resolution, natural language understanding, and decision-making under fuzzy criteria. Traditional code excels at math, deterministic operations, data retrieval, and anything with clear logic. If you can write a three-line function to solve a problem, don't make it an LLM tool. Let the computer do what computers are good at.
Rather than creating a tool for getting the current date, simply inject it into your prompt: "Today is January 20th, 2026." The model has the information it needs without an unnecessary tool call. This principle extends to other simple operations: formatting dates, basic arithmetic, string manipulation, and data lookups that don't require reasoning.
One consideration is prompt caching. If you inject dynamic information at the top of your prompt, you reduce cache effectiveness. If you can place it at the bottom after stable instructions, you get better cache hit rates. This tradeoff is worth considering for high-volume systems, but the fundamental principle remains the same: minimize LLM calls for non-reasoning tasks.
The broader lesson is to distinguish between complexity that requires intelligence and complexity that just feels complicated. Parsing a date format feels like work, but it's deterministic work that any programming language handles trivially. Deciding whether a customer's complaint requires immediate escalation based on tone, urgency, and context? That's genuinely ambiguous reasoning where LLMs add value.
In 2024 and early 2025, we expected fine-tuning to be essential for production systems. We were wrong. As foundation models have improved, prompt engineering has proven surprisingly durable and effective. This represents a significant shift in best practices compared to even a year ago.
The Anthropic Claude model family illustrates this evolution. When we upgraded customers from Claude 3.5 Sonnet to Claude 3.7, we saw some regressions that required prompt adjustments. But from Claude 3.7 to Claude 4 and then to Claude 4.5, we experienced essentially zero regressions across our evaluation sets. Many prompts worked as drop-in replacements with better performance, faster inference, and lower costs. This suggests the era of prompt brittleness across model versions may be ending, though we'll need more data points to confirm the trend holds.
Why has prompt engineering become more effective? Models became dramatically more powerful, instruction-following improved substantially, context windows expanded, and models got better at following complex structured output specifications. Together, these improvements mean that careful prompt engineering can achieve results that previously required fine-tuning.
Prompt optimization in production focuses on several areas. If your model generates verbose responses when concise ones would suffice, you're wasting money. We recommend you:
The practical implication is that the barrier to entry for production-grade generative AI has decreased. You don't need a team of Machine Learning engineers doing fine-tuning experiments. You need people who understand the problem domain, can write clear specifications, and can iterate on prompts systematically with proper evaluation coverage. This democratizes generative AI development, allowing teams without deep ML expertise to build production-grade systems.
Cost blindness kills generative AI projects after launch. A system that works beautifully in development with ten test users can bankrupt your project when it scales to thousands of real users. Understanding your unit economics isn't optional, it's foundational.
Prompt caching reduces costs by caching the prefix of your prompt across multiple requests. If your system instructions remain stable and only the user query changes, structure your prompt so the stable portion comes first. Amazon Bedrock and other providers automatically cache these prefixes, significantly reducing input token costs for cached portions. The strategic question becomes where to place dynamic information. Putting the current date or user context at the top breaks caching. Putting it at the bottom after instructions preserves cache effectiveness. The tradeoff depends on your traffic patterns and how critical that information is to response quality.
Batch inference offers dramatic cost savings when you don't need real-time responses. Amazon Bedrock provides batch inference at a 50% discount across all models. The architecture typically involves queuing requests in Amazon SQS, processing them in batches via AWS Lambda or Amazon ECS, and storing results for later retrieval. This works well for analysis jobs, content generation pipelines, or any workflow where latency in the range of minutes to hours is acceptable. The cost savings at scale justify the additional architectural complexity.
Context optimization is the process of identifying the minimum viable context needed for correct inference. Start with comprehensive context, then iteratively remove information while monitoring whether eval performance degrades. Often, you're injecting far more context than necessary because it feels safer. But every token costs money and increases latency. Testing which context elements actually improve outcomes versus which are just noise improves both cost and performance. Use A/B testing with your eval set to validate which parts of the context are safe to prune.
Compute selection matters for high-volume deployments. AWS Trainium and Inferentia offer approximately 60% price-performance improvement over Nvidia GPUs for supported models. The requirements are to use the Neuron SDK and accept RAM constraints. For models that fit these constraints and use cases with high volume, the cost savings compound quickly. You need to validate model compatibility and performance, but the economics often justify the migration effort.
Model selection by use case seems obvious, but it is surprisingly often neglected. Not every request needs your most powerful model. If a query is simple classification or extraction, a smaller model like Anthropic Claude Haiku might be sufficient, and it comes at a fraction of the cost. If a query requires deep reasoning or complex synthesis, the larger models justify their premium. Implementing routing logic that directs queries to appropriately sized models based on complexity improves economics without sacrificing quality.
Let's look at concrete numbers. Assume you're processing 1 million requests per month with an average of 1000 input tokens and 500 output tokens per request:
Baseline scenario (all requests to Claude 4.5 Opus):
Optimized scenario (smart routing + caching + selective batching):
60% to Claude 4.5 Haiku (simple queries): 600K requests
30% to Claude 4.5 Sonnet (moderate complexity): 300K requests
10% to Claude 4.5 Opus (complex reasoning): 100K requests
20% routed to batch (50% discount applied to above)
Total optimized: ~$5,890/month
This optimization reduces costs by roughly 66% while maintaining quality through appropriate model selection. The key is to instrument your system to understand request-complexity patterns and implement smart routing based on that data.
The overarching principle is to monitor cost per request, understand your unit economics, and set budgets and alerts. A successful demo becomes an expensive mistake if you discover only after launch that your cost per request exceeds the value you provide to users, or how much you can charge for that value. Build cost monitoring into your system from the beginning, track token usage patterns, and optimize iteratively as you learn which requests consume disproportionate resources.
At Caylent, we help organizations move from experimentation to production with confidence. Our team has deep, hands-on experience designing and scaling agentic AI systems, leveraging our expertise in machine learning and generative AI to build solutions that perform reliably in real-world environments. By harnessing the power of AWS technologies, our dedicated Generative AI practice empowers enterprises to accelerate innovation through offerings like our Generative AI Strategy and Generative AI Knowledge Base. Whether you’re building your first agentic workflow or evolving a complex multi-agent architecture, Caylent helps you turn intelligent automation into lasting business impact.
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Randall Hunt
Guille Ojeda