A $5 AI budget for my next technical interview

Start with the interview and a spending ceiling

AI can generate endless interview questions. That is useful only if you know what you need to practice and when to stop. Before opening a conversation, write down the role, interview format, likely topics, and days remaining. Use the job description and recruiter guidance rather than asking a model to guess the company’s process.

Next, choose a total practice budget. Five dollars could be your personal ceiling, not a promise about how many sessions it will buy. Reserve part of it for a final mock interview. Treat spending as a limit, not a target you must reach.

Run one short diagnostic before building a schedule

Ask the model to conduct a twenty-minute diagnostic covering the skills your interview actually tests. For a junior backend role, that might mean one data-structure problem, one database question, and one explanation of a past project. Request one question at a time, with no solution until you have attempted an answer.

Afterward, ask it to identify three weaknesses using evidence from your responses. A useful finding is that you skipped duplicate values when explaining a search algorithm. A vague judgment that you need stronger fundamentals gives you little to act on. Verify technical claims against reliable references.

Turn the weakest skill into a small practice loop

Choose the most important gap and request a problem that exercises it without adding unrelated difficulty. State your preferred language, expected difficulty, and time limit. Ask the model to keep its initial response to the problem statement and constraints.

Then work independently. Explain your assumptions, outline an approach, and implement it in your usual environment. Run tests yourself. If you get stuck, request one hint rather than the answer. This preserves the thinking practice that an interview is meant to assess and avoids paying for a long solution you will only read.

Ask for evidence instead of a flattering score

Once your attempt is complete, share the relevant code and reasoning. Request feedback on correctness, complexity, edge cases, and clarity. Ask the model to quote the specific part of your answer behind each criticism and suggest the smallest useful correction.

Do not accept a passing verdict as proof. Test empty inputs, boundary values, and any counterexample the model proposes. Then explain the corrected approach aloud without looking at the feedback. If you cannot reconstruct it, repeat the concept with a different problem rather than memorizing the generated solution.

Practice design and behavioral answers with constraints

For system design, ask the model to play an interviewer who reveals requirements only when asked. Begin with users, scale, and reliability needs before proposing components. Request one challenge at a time, such as a traffic spike or a failed dependency, so you can defend trade-offs rather than recite an architecture.

For behavioral practice, use real experiences with identifying details removed. Ask follow-up questions about your contribution, decisions, and results. Have the model flag missing evidence, not invent achievements or metrics. Your final answer should sound like something you can honestly discuss under questioning.

Review request costs before adding more practice

Keep routine drills on one model initially; DeepSeek V4 Flash is one option to test. Compare the usefulness of its feedback with the actual request cost rather than assuming every task needs a different model. Keep prompts focused and ask for concise feedback.

MyRelay gives an individual one key and one budget across supported models, with token and cost details for every request. Create a MyRelay account if that visibility would help you manage your practice budget. After two sessions, inspect your spending and shorten oversized responses or repeated context before continuing.

Finish with an unaided mock and a short review

Use your reserved budget for one timed mock that matches the expected interview format. Ask the model to withhold coaching until the end. Then record three things: what you solved independently, where you needed help, and what you will review next. Stop when the planned session ends. Progress means better independent reasoning, not a longer chat history.