I’m putting together an AI feature for a small product and keep getting pulled in five directions. OpenAI, Claude, Grok, Perplexity, and DeepSeek all come up in recommendations, but the advice often seems tied to someone else’s use case—or to a model comparison that’s already out of date.
The feature I’m working on needs to answer questions from a mix of user-provided documents and general information. Some requests need careful writing, some need reliable source discovery, and some are simple enough that speed and cost matter more than a long, polished response. I may eventually connect everything through an API, but I’d rather not build around a model before I understand what actually matters.
What would you compare first?
I’m trying to compare AI models on real tasks rather than judge them by a handful of impressive demos. I can prepare a small set of representative questions, but I’m unsure how broad that test should be. Should I score factual accuracy, instruction following, citation quality, latency, and cost separately? How do you account for answers that sound confident but miss an important detail?
The tools also seem to serve different jobs. Perplexity is often discussed in the context of finding and presenting sources, while a model accessed through an API may fit better into an existing product flow. Claude and OpenAI models get recommended for different writing and reasoning tasks; Grok and DeepSeek come up in other comparisons. I don’t want to assume those reputations tell me how any current model will perform on my own prompts.
Prompt quality versus model choice
I’m also wondering how much of the result comes down to the prompt. A vague request can make a capable model look unreliable, but piling on instructions can make prompts harder to maintain. For this feature, I could give each model the same LLM prompt evaluation set, including examples where the right response is to say that the supplied documents don’t contain enough information.
Would you keep one shared prompt across providers, or tailor it for each model? I’m interested in practical ways to test prompt consistency across models without accidentally giving one provider an advantage through a prompt written specifically for it.
Planning the API integration
If I add providers, I’d like the application to have a simple interface rather than model-specific logic scattered throughout the code. But OpenAI and Claude APIs, along with other provider APIs, may differ in request formats, tool support, streaming behavior, and how they handle errors. A common wrapper sounds useful, though I’m wary of hiding features that matter or making debugging harder.
For anyone who has built something similar, how did you approach API integration for multiple LLMs? Did you route requests by task, let users choose, or begin with one provider and add a second only after measuring a clear need? I’d also like to know what you tracked after launch: response time, token usage, failed requests, user corrections, or something else.
- How do you measure LLM response quality on a small but useful evaluation set?
- When does Perplexity for source discovery make more sense than asking a general-purpose model?
- Has anyone found a meaningful use for Grok for current information in a product workflow, rather than just casual research?
- What should I test when DeepSeek API integration is one of several possible routes?
I’m not looking for a single winner across every task. I’d like to hear how people choose a model for a specific workflow, keep the comparison fair as providers change, and avoid spending weeks building an abstraction that solves a problem they didn’t have. What would you test first?
