GenAI Strategy for Startups

Every startup CTO is being asked about their GenAI strategy. The pressure comes from investors, boards, and the fear of being left behind. The result is often a scramble to bolt LLMs onto existing products, regardless of whether it makes sense. This leads to AI theater: features that demo well but provide little real value, shipped under the assumption that AI presence equals competitive advantage.

The reality is more nuanced. GenAI can create genuine value, but only when applied to the right problems with realistic expectations about costs, capabilities, and maintenance burden. This post outlines a practical framework for making GenAI decisions in resource-constrained environments.

Build vs Buy: The First Decision

The default answer should be buy. Unless your core product is the LLM itself, you are almost certainly better off using existing APIs than training or fine-tuning your own models. The exceptions are narrow and specific.

Use existing APIs (OpenAI, Anthropic, Google) when you need general-purpose language understanding, summarization, or generation. These models are remarkably good at zero-shot and few-shot tasks. With proper prompt engineering and retrieval-augmented generation, you can handle most use cases without touching model weights. Prompt engineering is underrated. Spending a week refining prompts often yields better results than spending a month fine-tuning a model. The best prompt engineers I've worked with treat it as a craft: systematic testing, version control, regression suites.

Consider building when you have domain-specific requirements that general models consistently fail on, when you need deterministic behavior that API updates might break, or when you have genuine data moats that justify the investment. Even then, start with open-source models (Llama, Mistral) before considering training from scratch.

The build decision comes with hidden costs. You need MLOps infrastructure, monitoring, retraining pipelines, and engineers who understand transformers. For most startups, this is a distraction from core product work. Save your engineering budget for differentiation that matters.

Identifying High-Value Use Cases

The best GenAI features share common characteristics: they solve real user problems, they improve measurably on existing solutions, and they degrade gracefully when the model is wrong.

Look for tasks that are repetitive, time-consuming, and currently done manually. Document summarization, email triage, data extraction from unstructured text, customer support routing. These are defensible use cases because the baseline is human time, which is expensive and scarce.

Avoid use cases where accuracy requirements are binary. If a 95% success rate means 5% catastrophic failures, LLMs are the wrong tool. Examples include legal document generation without review, medical diagnosis, or financial calculations. The probabilistic nature of language models makes them poorly suited for tasks where errors compound or have regulatory implications.

The highest-value use cases often involve augmentation rather than replacement. Give your users a draft, not a final output. Provide suggestions, not decisions. This framing manages expectations and creates space for the model to be wrong without breaking the user experience.

Cost Modeling: API Costs Scale Surprisingly Fast

API costs are easy to underestimate. A feature that costs pennies per request in testing can balloon to thousands of dollars per day in production. The culprits are usually token-heavy operations: long contexts, multi-turn conversations, or retrieval systems that stuff dozens of documents into every prompt.

Do the math before you ship. If your average request uses 2,000 input tokens and 500 output tokens, and you're using GPT-4, that's roughly $0.03 per request. At 10,000 requests per day, you're spending $300 daily, or $9,000 monthly, on API calls alone. Scale to 100,000 requests and you're at $90,000 per month. These numbers add up faster than your AWS bill. I've seen teams launch features without tracking per-request costs, only to discover weeks later that their burn rate had doubled. Token usage is not intuitive. Long contexts, chain-of-thought reasoning, and retrieval all increase costs in ways that aren't obvious until you're in production.

Optimize aggressively. Use smaller models (GPT-3.5, Claude Haiku) for tasks that don't require reasoning depth. Cache embeddings and reuse them. Compress prompts by removing redundant context. Batch requests where possible. These optimizations can cut costs by 10x without sacrificing much quality.

Build cost guardrails into your product. Rate-limit expensive operations. Monitor token usage per user and flag outliers. Set budget alerts. Treating API costs as variable and unbounded is a recipe for unpleasant surprises.

Fine-Tuning: When and Why

Fine-tuning is over-hyped. For most use cases, you don't need it. Prompt engineering and retrieval-augmented generation solve 90% of problems without touching model weights. Fine-tuning makes sense only when you've exhausted those options and still see consistent failure modes.

Fine-tune when you need consistent formatting that prompts can't enforce, when domain-specific vocabulary or jargon isn't well-represented in the base model, or when latency matters and you can use a smaller fine-tuned model instead of a larger general-purpose one. Examples include code generation for proprietary languages, entity extraction from industry-specific documents, or style matching for branded content.

The prerequisites for successful fine-tuning are strict. You need labeled data—thousands of examples, not dozens. You need evaluation metrics that capture what you care about. You need infrastructure for training, versioning, and serving models. And you need the discipline to retrain regularly as your data distribution shifts.

Most startups don't have this infrastructure and shouldn't build it. If you're serious about fine-tuning, use managed services (OpenAI's fine-tuning API, AWS SageMaker, Modal) rather than rolling your own. The complexity of managing training jobs, hyperparameter tuning, and model deployment is not where you want to spend engineering cycles.

RAG vs Fine-Tuning: A Decision Framework

RAG and fine-tuning solve different problems. RAG injects external knowledge at inference time by retrieving relevant documents and including them in the prompt. Fine-tuning bakes knowledge into the model weights during training. Understanding when to use each is critical.

Use RAG when your knowledge base changes frequently, when you need citations or source attribution, when you want to control exactly what information the model can access, or when you're working with proprietary or sensitive data that you don't want embedded in model weights. RAG is also cheaper to iterate on—you can update your retrieval corpus without retraining anything.

Use fine-tuning when you need behavioral changes (tone, style, formatting) rather than new knowledge, when domain-specific reasoning patterns aren't captured by general models, or when latency is critical and you can't afford the overhead of retrieval and long prompts. Fine-tuning is also useful when your task requires understanding of concepts that are difficult to express in natural language prompts.

In practice, many systems use both. RAG provides the knowledge, fine-tuning provides the style and reasoning. But if you're forced to choose one, start with RAG. It's more flexible, easier to debug, and doesn't lock you into a specific model architecture. RAG systems fail in predictable ways. Retrieval quality is the usual bottleneck: chunking strategies, embedding models, and query formulation all matter. But these are debuggable problems. Fine-tuning failures are harder to diagnose because the errors are baked into the weights.

Avoiding Vendor Lock-In

Vendor lock-in is real. If you build your entire product on OpenAI's API and they change pricing, deprecate models, or go down for extended periods, you're stuck. The same applies to Anthropic, Google, or any single provider.

Design for portability from day one. Abstract your LLM calls behind a provider interface. Use libraries like LangChain or LiteLLM that support multiple backends, or write your own thin wrapper. The goal is to make switching providers a configuration change, not a code rewrite.

Test against multiple providers regularly. Even if you're using OpenAI in production, run evaluations against Claude or Gemini to ensure you could switch if needed. This also helps you stay informed about relative capabilities and pricing as the landscape evolves.

For critical features, maintain fallback options. If your primary provider is down, can you degrade gracefully to a simpler heuristic, queue requests for later, or switch to a backup provider? Availability SLAs from LLM vendors are not enterprise-grade. Plan accordingly.

MVP Approach to GenAI Features

Ship the simplest possible version first. GenAI features are particularly prone to over-engineering because the capabilities feel limitless. Resist the temptation to build complex multi-agent systems or elaborate prompt chains before you've validated that users want the feature at all.

Start with a single prompt and a single model. No retrieval, no fine-tuning, no orchestration. See if it solves the user's problem. If it doesn't, adding complexity won't help. If it does, you've learned what matters and can iterate from there.

Use wizard-of-oz testing for uncertain use cases. Have humans generate the outputs you're planning to automate and see if users value them. This is particularly useful for features where the model's success is hard to predict. You'll learn faster by faking it than by building it.

Measure everything. Track success rates, user satisfaction, task completion, and costs from day one. GenAI features often feel magical but fail to move core metrics. If you can't measure impact, you can't justify the investment.

Red Flags in GenAI Project Proposals

Some proposals are doomed from the start. Learning to recognize them saves time and credibility. Here are patterns I've seen repeatedly that predict failure.

No clear success metric. If the proposal doesn't specify how you'll measure whether the feature works, it's not ready to build. Vague goals like "improve user experience" or "leverage AI" are not sufficient. You need numbers: accuracy targets, latency requirements, cost budgets.

Replacing humans in high-stakes decisions without review workflows. Proposals that assume LLMs can make final decisions in domains like legal, medical, or financial services ignore both the technology's limitations and the regulatory landscape. The path forward involves augmentation and review, not full automation.

Assuming fine-tuning solves everything. If the proposal jumps straight to fine-tuning without explaining why prompts and RAG won't work, it's likely cargo-culting. Fine-tuning is a tool of last resort, not a first line of attack.

Ignoring data requirements. Proposals that handwave away data collection, labeling, and quality are fantasy. If you don't have the data, you can't build the feature. And "we'll scrape it" or "we'll use GPT-4 to label it" rarely works as well as hoped.

No fallback for when the model is wrong. LLMs fail. They hallucinate, they misunderstand, they produce nonsense. If the proposal doesn't address how the system handles these failures, it's incomplete. Error handling is not an afterthought.

Closing Thoughts

GenAI is a tool, not a strategy. The startups that succeed with it are those that apply it to genuine problems with clear metrics and realistic expectations. The ones that fail are those chasing hype, bolting LLMs onto products where they don't belong, and ignoring costs until they become unsustainable.

Start small. Ship fast. Measure everything. Avoid lock-in. Treat API costs as a first-class concern. Don't fine-tune unless you've exhausted simpler options. And above all, be honest about whether AI is solving a real problem or just making your pitch deck more compelling.

The best time to build a GenAI strategy was before the hype cycle. The second-best time is now, with a clear head and a focus on fundamentals.