Top 3 AI Engineering Tasks You Need to Know

To build or enhance an AI-powered application today, you rarely need to train a model from scratch. Instead, you can tap into model-as-a-service platforms such as the OpenAI API. The heavy lifting—pretraining, infrastructure, scalability—is already handled by the provider. As AI engineers, this frees us to focus on three critical tasks that make our apps reliable, user-friendly, and production-ready. In this article, we’ll explore those three pillars of modern AI engineering.

Model Evaluation

The most important task as an AI engineer is to evaluate models to pick the right model for your application. These are the most important metrics you should consider when you want to adapt a model in your application:

1. Costs: for example, how much they charge you per input/output token
2. Performance: Model latency and its impact on UX
3. Model reasoning and intelligence grade
4. Response accuracy and faithfulness to the context you provided to the model

Here, it can be a handy checklist for you to evaluate models:

1. List your candidates: Open Source, As a Service, and on-prem solutions
2. Prepare a sample of data
3. Run a quick Smoke Test on models and sanity check their answers
4. Filter models based on their response
5. Compare their Cost, Performance, and their Intelligence 
6. Pick the one that aligns with your budget, performance, and intelligence metrics

Let's say you want to implement a customer support chatbot that answers your clients' questions about your product. In this case, you need a model that can respond quickly to your customers, can extract data from your existing documents, and doesn't have a high cost.

You can prepare example inputs, using historical real clients' questions, or you can make some input and run your smoke test, and see which model answers correctly to the queries. In this way, you shortlist the models you prepared in step one.

Prompt engineering

Prompt engineering is more than “just” writing English instructions—it’s a structured process that turns a generic large-language model into a reliable, domain-savvy assistant. A few well-crafted lines can dramatically boost accuracy, reduce “hallucinations,” and cut your downstream filtering work in half. Here’s how to do it:

Anatomy of an Effective Prompt  

Role and Context

Start with “You are a …” to set the model’s persona. For example: “You are an expert financial advisor with 10 years of experience.”

Task Definition

Clearly state what you want: “Summarize the following transcript in bullet points.”

Constraints & Formatting

Limit length: “Keep your answer under 100 words.” Specify style: “Use plain language, no jargon.”

Evaluate your prompts

Using tools like promptlayer.com to version your prompts, log metrics, and track their versions.

Interface Development

This is the part that requires full-stack development skills to implement a UI that users can interact with, and the UI is wired up to your backend, where it talks to model APIs.