DEV Community: Renekin AI

25 RAG interview questions with answers, from basics to production

Renekin AI — Tue, 27 Oct 2026 11:30:00 +0000

If you're interviewing for an AI engineer or GenAI developer role, you will be asked about RAG. It's the most common way companies put LLMs to work on their own data, so it's the topic interviewers dig into hardest.

These 25 questions follow the RAG pipeline from start to finish. The answers are short on purpose: use them as a base, and add what you saw in your own projects.

The basics

1. What is RAG, and why use it instead of just prompting?

RAG retrieves relevant pieces of your documents at query time and adds them to the prompt. The model then answers from that context. It lets an LLM use private or recent data it was never trained on, reduces hallucinations, and makes citations possible.

2. Walk me through a RAG pipeline.

Ingestion: load documents, clean them, split them into chunks, embed each chunk and store it in an index with metadata. Query time: embed the question, retrieve the most similar chunks (often with keyword search too), rerank, build a prompt with the top chunks, generate the answer with citations.

3. RAG or fine-tuning?

RAG adds knowledge , especially knowledge that changes or must be cited. Fine-tuning changes behaviour : format, tone, or a narrow task. To teach a model your company's latest policies, use RAG. To make it always reply in a strict JSON format with a smaller model, consider fine-tuning.

4. What are embeddings?

Vectors that capture meaning, so texts with similar meaning end up close together. Retrieval works by embedding the query and finding the chunks whose vectors are closest, usually by cosine similarity.

Chunking and ingestion

5. How do you choose a chunk size?

Start with a few hundred tokens and some overlap, splitting on natural boundaries like headings and paragraphs. Then tune with an evaluation set. Small chunks match precisely but lose context; big chunks keep context but add noise and cost.

6. Why use chunk overlap?

So a sentence or idea that falls on a boundary still appears whole in at least one chunk. Too much overlap wastes storage and returns near-duplicate chunks.

7. How do you handle tables, PDFs and code?

Parse structure instead of flattening everything to text. Keep tables as rows with their headers, extract PDF text with a layout-aware parser (and OCR for scanned files), and split code by functions or classes rather than by character count.

8. What metadata would you store with each chunk?

Source, title, section heading, date, author or owner, and access permissions. Metadata enables filtering ("only HR documents from 2026"), citations, and making sure users only see documents they're allowed to.

9. How do you keep the index up to date?

Re-ingest changed documents incrementally, using content hashes or modified dates, and delete chunks of removed documents. For fast-changing sources, run a scheduled or event-driven pipeline.

Retrieval

10. What is hybrid search and why use it?

Combining keyword search (BM25) with vector search. Embeddings are great at paraphrases but weak on exact strings like error codes, product names and IDs. Keyword search catches those. Results are merged, often with reciprocal rank fusion.

11. What is a reranker?

A model, usually a cross-encoder, that reads the query and each candidate chunk together and scores how relevant the chunk is. It's slower but more accurate than embedding similarity, so you retrieve broadly (say 30 to 50 chunks) and rerank down to the best 3 to 8.

12. What is query rewriting?

Using an LLM to turn the user's question into a better search query: expanding abbreviations, adding context from the conversation, or splitting a compound question into several searches. It helps a lot in multi-turn chat, where "what about the second one?" isn't searchable on its own.

13. What is HyDE?

Hypothetical document embeddings: the LLM writes a fake answer to the question, and you embed that instead of the question. The fake answer often looks more like the real document than the question does, which can improve retrieval.

14. How do vector indexes like HNSW work?

They are approximate nearest-neighbour indexes. HNSW builds a layered graph where each vector links to its neighbours, so a search hops through the graph instead of comparing against every vector. You trade a little accuracy for a big speed-up.

15. How many chunks should you pass to the LLM?

As few as gives good answers. More chunks add cost, latency and distracting noise. Tune k with your evaluation set; reranking usually lets you pass fewer.

Generation

16. How do you make the model stick to the retrieved context?

Tell it to answer only from the provided context, to cite the source for each claim, and to say it doesn't know when the context doesn't cover the question. Put the context clearly separated from the instructions, and test with questions that have no answer in the documents.

17. How do you add citations?

Give each chunk an ID in the prompt and ask the model to reference IDs next to its claims. Map the IDs back to links or page numbers in your UI, and validate that cited IDs actually exist.

18. How do you handle questions that need information from many documents?

Retrieve per sub-question (query decomposition), summarise per document and then combine (map-reduce), or let an agent run several searches. For "compare X and Y" questions, retrieve for X and for Y separately.

Evaluation and debugging

19. How do you evaluate a RAG system?

Separately evaluate retrieval and generation. Retrieval: recall@k and precision on a labelled set of question-to-correct-chunk pairs. Generation: faithfulness (claims supported by context), answer relevance and correctness, often scored by an LLM judge that you check against human labels.

20. The answers are wrong. What do you check first?

Whether the right chunk was retrieved. Log the retrieved chunks for failing questions. If the right chunk is missing, fix ingestion, chunking, embeddings or search. If it's there and the answer is still wrong, fix the prompt, reduce noise, or use a stronger model.

21. How do you build a test set?

Collect 50 to 100 real questions from users or support tickets, plus edge cases and unanswerable questions. Label the correct source chunk and expected answer. Add every production failure to the set so it can't happen again.

22. What is "lost in the middle"?

A pattern seen in research where models use information at the start and end of a long context better than information in the middle. Practical fixes: pass fewer chunks, put the most relevant ones first, and rerank.

Production

23. How do you reduce latency in a RAG app?

Stream the answer, cache embeddings for repeated queries, cache answers for common questions, run keyword and vector searches in parallel, use a smaller model for query rewriting, and keep the context short.

24. How do you handle access control?

Store permissions as metadata and filter at retrieval time, before anything reaches the LLM. Never rely on the prompt to hide documents the user shouldn't see.

25. What are common security risks in RAG?

Prompt injection hidden in documents, leaking documents across users, and sensitive data ending up in logs. Treat retrieved text as untrusted, filter by permissions, limit tool access, and redact personal data where needed.

How to stand out

Everyone can define RAG. What gets offers is evidence: "our recall@5 was 64%, hybrid search and a reranker took it to 88%, and that cut wrong answers by about half." If you don't have a work project, build a small one with an evaluation set. Our 4-week AI engineer interview plan shows how.

For the full range of topics beyond RAG, see our 40 AI engineer interview questions.

How to prepare for an AI engineer interview: a 4-week plan

Renekin AI — Mon, 26 Oct 2026 11:30:00 +0000

Most people prepare for AI engineer interviews the wrong way: they watch transformer lectures for three weeks, then freeze when asked "how would you know your chatbot is giving good answers?"

The interviews have changed. Companies hiring AI engineers want people who can ship and measure LLM features, not recite papers. This plan is built around that. It assumes you can already write Python; if you're switching from backend or full-stack, you're in a great spot.

First, know what you're preparing for

An AI engineer interview loop usually has five parts:

Coding : Python, sometimes easy-to-medium DSA, sometimes a practical task like parsing data or calling an API.
Fundamentals : tokens, embeddings, attention, prompting, RAG, fine-tuning, evaluation.
AI system design : "Design a support bot over our docs" or "design an agent that books meetings".
Project deep dive : one project you built, explained in detail.
Behavioral : ownership, ambiguity, a failure, working with product teams.

Read the job description closely. "LLM", "RAG", "LangChain", "agents" and "evaluation" point to an applied role. "PyTorch", "training", "distributed" and "CUDA" point to a more research-heavy ML role, which needs deeper math.

Week 1: fundamentals and daily coding

Goal: explain how LLMs work and where they fail, in plain words.

Day 1-2: tokens, context windows, temperature, how a transformer and attention work at a high level, base vs instruction-tuned models.
Day 3-4: embeddings, vector search, cosine similarity, chunking, hybrid search, rerankers.
Day 5-6: fine-tuning basics (LoRA, quantization), when to fine-tune vs use RAG, hallucination and how to reduce it.
Day 7: review. Explain each topic out loud in two minutes, as if to a product manager.

Every day, do 1 or 2 coding problems in Python. Focus on arrays, strings, hash maps and simple async code. Our list of 40 AI engineer interview questions works as your checklist for this week.

Week 2: build one project that answers half the interview

Goal: a small, real project you can talk about for 20 minutes.

Build a RAG app over real documents : your college's placement handbook, a company's public docs, a set of research papers. Keep it simple:

Load and chunk the documents.
Embed them and store them in a vector database (or even a NumPy array).
Retrieve the top chunks for a question and generate an answer with citations.
Build an evaluation set of 30 to 50 questions with expected answers.
Measure, change one thing (chunk size, hybrid search, a reranker, the prompt), and measure again.

The evaluation set is what separates you from other candidates. "I changed the chunk size from 1,000 to 400 tokens and retrieval recall went from 62% to 81%" is a sentence interviewers remember.

Write a clear README: the problem, the architecture, what you tried, the numbers, and what you'd do next. Put it on GitHub and at the top of your resume.

Week 3: AI system design

Goal: design an LLM system on a whiteboard, with trade-offs.

Practise a framework you can repeat:

Clarify : who are the users, what does a good answer look like, how many requests, what latency is acceptable?
High-level design : client, API, retrieval, model, tools, storage.
Data and retrieval : sources, chunking, indexing, keeping it fresh, access control.
Model choice : big vs small model, routing, cost per request.
Evaluation : offline test set, online feedback, guardrails.
Production : caching, streaming, rate limits, fallbacks, monitoring.

Practise on 4 or 5 prompts: a document Q&A bot, a customer support assistant, an AI code reviewer, a meeting summariser, an agent that files expense reports. Say your answers out loud and time yourself at 35 to 40 minutes.

Week 4: mocks, stories and the company

Goal: sound calm and specific under pressure.

Two or three mock interviews. A friend, a senior or an online mock platform. Record yourself if no one is available. Most people only notice their filler words and rambling when they watch it back.
Six behavioral stories in STAR format: a project you're proud of, a failure, a conflict, a time you learned something fast, a time you disagreed, a time you took ownership. Our STAR method answer generator turns a rough story into a structured answer.
"Tell me about yourself" : a 60 to 90 second version that ends with why this role. Try the Tell Me About Yourself generator.
Company research : their product, where AI fits into it, and one idea for how you'd improve it. Then predict their questions by pasting the job description into the interview question predictor.

Don't forget the resume

All of this only matters if you get the call. Before you apply:

Put your project with numbers near the top.
Mirror the job description's wording for skills you actually have (Python, RAG, vector databases, evaluation, the specific LLM APIs).
Keep it one column and ATS-friendly. Our guide on how to beat ATS covers the format.

Then run it through the free ATS resume checker against the job description to see which keywords you're missing.

The short version

Week	Focus	Output
1	Fundamentals + daily Python	Can explain 30+ concepts in plain words
2	One RAG project with an eval set	GitHub repo with numbers in the README
3	AI system design	5 practised designs, 40 minutes each
4	Mocks, stories, research	6 STAR stories, a 90-second intro, 2-3 mocks

Four weeks is enough if you spend them building and practising out loud, not just reading.

40 AI engineer interview questions (and how to answer them) for 2026

Renekin AI — Sun, 25 Oct 2026 11:30:00 +0000

"AI engineer" barely existed as a job title three years ago. Now it's one of the most common roles on job boards, and the interviews have settled into a clear pattern.

We went through the questions that keep showing up across startups, product companies and big tech, and grouped them into the 40 you should be ready for. Each one has a short answer. Treat it as a skeleton: the best answers add a line like "in my project, I did X and saw Y".

What the AI engineer interview loop looks like

Most loops have some version of these rounds:

Round	What they test
Coding	Python, data handling, sometimes easy-to-medium DSA
Fundamentals	LLMs, embeddings, prompting, basic ML
AI system design	Design a RAG app, an agent, or a support bot end to end
Project deep dive	One project you built, in detail
Behavioral	Ownership, ambiguity, working with product and data teams

Let's go through the questions round by round.

LLM fundamentals

1. What is a token, and why does it matter?

A token is the unit a model reads and writes, roughly a word piece. In English one token is about four characters on average. Tokens matter because context limits, latency and price are all counted in tokens, so long prompts cost more and run slower.

2. Explain how a transformer works in simple terms.

It turns tokens into vectors, then passes them through layers of self-attention and feed-forward networks. Self-attention lets each token look at every other token and decide which ones matter for its meaning. A decoder-only model like GPT or Claude predicts the next token, one at a time.

3. What is attention, and why was it a big deal?

Attention computes, for each token, a weighted mix of all other tokens using query, key and value vectors. Unlike RNNs, it processes the whole sequence in parallel and handles long-range dependencies well, which made training on huge datasets practical.

4. What is a context window?

The maximum number of tokens the model can consider at once, prompt plus output. A bigger window lets you pass more documents, but models can still miss details buried in the middle of a long context, and cost grows with every token.

5. What do temperature and top-p do?

Both control randomness when picking the next token. Low temperature makes output more predictable; high temperature makes it more varied. Top-p limits sampling to the smallest set of tokens whose probabilities add up to p. For extraction or classification use low values; for brainstorming, higher.

6. Why do LLMs hallucinate?

They are trained to produce likely text, not verified facts. When the answer isn't in their training data or context, they still produce something fluent. You reduce it with grounding (RAG), asking for citations, allowing "I don't know", constraining output formats, and evaluating for faithfulness.

7. What is a KV cache?

During generation the model stores the keys and values of tokens it has already processed, so it doesn't recompute them for every new token. It's why generating the first token is slower than the rest, and why long contexts use a lot of GPU memory.

8. What is the difference between a base model and an instruction-tuned model?

A base model only continues text. An instruction-tuned model has been further trained (supervised fine-tuning, then preference tuning such as RLHF or DPO) to follow instructions and hold a conversation. Products almost always use instruction-tuned models.

Prompting and RAG

9. What is RAG and when would you use it?

Retrieval-augmented generation fetches relevant documents at query time and puts them in the prompt. Use it when answers depend on private, large or frequently changing data, and when you need citations. It's usually cheaper and faster to update than fine-tuning.

10. What are embeddings?

Vectors that represent the meaning of text, so similar meanings sit close together. You embed your documents once, embed each query, and find the nearest documents with cosine similarity or dot product.

11. How do you choose a chunk size?

It depends on the documents and questions. Small chunks give precise matches but lose context; large chunks keep context but add noise and cost. A common start is a few hundred tokens with some overlap, split on headings or paragraphs, then tune it using your evaluation set.

12. What is hybrid search?

Combining keyword search (like BM25) with vector search. Keywords catch exact terms such as product codes, names and error messages that embeddings can miss; vectors catch paraphrases. Results are merged, often with reciprocal rank fusion.

13. What is a reranker?

A second model, usually a cross-encoder, that scores each retrieved chunk against the query more accurately than the first retrieval step. You retrieve, say, 30 candidates fast, rerank them, and pass only the top 5 to the LLM.

14. Your RAG app gives wrong answers. How do you debug it?

Split the problem in two. First check retrieval: was the right chunk in the results? If not, fix chunking, embeddings, hybrid search or metadata filters. If the right chunk was there, the problem is generation: fix the prompt, reduce noise in the context, or use a stronger model.

15. What is prompt injection, and how do you defend against it?

Text in user input or retrieved documents that tries to override your instructions. Defend with layers: keep instructions separate from untrusted content, limit what tools the model can call, require confirmation for risky actions, validate outputs, and never put secrets in the prompt.

16. Zero-shot vs few-shot prompting?

Zero-shot gives only instructions. Few-shot adds a handful of input-output examples. Few-shot helps most with format and tone, but adds tokens, so pick examples that cover the tricky cases.

Fine-tuning and adapting models

17. RAG or fine-tuning: how do you decide?

Use RAG to give the model knowledge, especially knowledge that changes. Use fine-tuning to change behaviour: a format, a style, a narrow task done cheaply by a smaller model. Many production systems use both, and most should start with prompting plus RAG.

18. What is LoRA?

Low-rank adaptation. Instead of updating all the weights, you train small low-rank matrices added to some layers. It needs far less memory and storage, and you can swap adapters per task. QLoRA does the same on a quantized base model to save even more memory.

19. What is quantization?

Storing weights in fewer bits, such as 8-bit or 4-bit instead of 16-bit. It cuts memory and often speeds up inference, with some quality loss that you should measure on your own tasks.

20. What is distillation?

Training a smaller "student" model to copy a larger "teacher" model's outputs. You get most of the quality on a narrow task at a fraction of the cost and latency.

21. How much data do you need to fine-tune?

For a narrow format or style task, a few hundred to a few thousand high-quality examples often work. Quality matters more than quantity: clean, consistent, representative examples beat a big noisy dump.

22. What is RLHF, and what is DPO?

RLHF trains a reward model on human preferences, then optimises the LLM against it with reinforcement learning. DPO (direct preference optimisation) skips the separate reward model and learns directly from pairs of preferred and rejected answers. It's simpler and widely used.

Evaluation

23. How do you evaluate an LLM application?

Build a test set of real inputs with expected outcomes, define metrics for what matters (correctness, faithfulness, format, safety, latency, cost), and run it on every change. Combine automated checks, LLM-as-judge for fuzzy qualities, and regular human review.

24. What is LLM-as-a-judge, and what are its risks?

Using a model to grade outputs. It scales well but has biases: it can favour longer answers, the first option shown, or its own style. Use clear rubrics, compare against human labels on a sample, and randomise the order of options.

25. What metrics would you use for a RAG system?

For retrieval: recall@k (is the right chunk in the top k?) and precision. For generation: faithfulness (is every claim supported by the context?), answer relevance, and correctness against a reference. Plus latency and cost per query.

26. How do you build an evaluation set without much data?

Start with 30 to 100 real or realistic questions, including edge cases and questions that should be refused. Add every production failure to the set. Synthetic questions generated from your documents are fine to start, but check them by hand.

27. What is the difference between offline and online evaluation?

Offline runs on a fixed test set before release. Online measures real usage after release: thumbs up or down, retries, task completion, escalations to humans, and A/B tests.

28. A new model version scores higher on benchmarks. Do you switch?

Not just because of public benchmarks. Run it on your own eval set, check latency, cost and output format, and watch for regressions on cases that used to pass. Roll it out gradually.

Production, cost and agents

29. How do you reduce LLM latency?

Stream the output, shorten prompts, use a smaller or faster model where quality allows, cache repeated prompts and answers, run independent calls in parallel, and cap output length.

30. How do you reduce cost?

Route easy requests to small models, use prompt caching for long fixed system prompts, trim retrieved context, batch offline jobs, and set token limits. Track cost per request and per user from day one.

31. What is function calling (tool use)?

The model returns a structured request to call a function you defined, such as a search or a database query. Your code runs it and sends the result back. It's how LLMs take actions and fetch live data reliably.

32. What is an AI agent?

A loop where the model plans, calls tools, looks at the results and decides the next step until the task is done. Agents are powerful but harder to control, so add step limits, clear tool descriptions, logging, and human approval for risky actions.

33. How do you get reliable JSON out of an LLM?

Use the provider's structured output or tool-calling features where available, give a schema and an example, validate the result in code, and retry with the validation error if it fails.

34. What do you monitor in production?

Latency, error rates, token usage and cost, user feedback, and quality on sampled traffic. Log prompts and responses (respecting privacy) so you can reproduce bad answers.

35. How do you handle rate limits and provider outages?

Retries with exponential backoff, queues for non-urgent work, a fallback model or provider, timeouts, and graceful messages to users.

Coding round

36. Write a function that splits text into overlapping chunks.

They want clean Python: a window over tokens or words, a step of chunk size minus overlap, and handling for the last chunk and empty input. Talk about splitting on paragraphs or sentences before falling back to a fixed size.

37. Implement cosine similarity and top-k search with NumPy.

Normalise vectors, take a dot product between the query and the matrix, then use argsort or argpartition for the top k. Mention that at scale you'd use a vector index (HNSW, IVF) instead of brute force.

38. Call an LLM API for 1,000 documents as fast as possible.

Use async requests with a concurrency limit (a semaphore), retries with backoff, and save results as you go so a failure doesn't lose everything.

Behavioral

39. Tell me about an AI project that didn't work.

Pick a real one. Explain what you expected, how you found out it wasn't working (ideally with an eval), what you changed, and what you'd do differently. Interviewers want to see that you measure rather than guess.

40. How do you explain an AI system's limits to non-technical stakeholders?

Show examples of where it fails, give a simple accuracy number from your eval set, and agree on what happens when it's wrong: human review, a fallback, or a clear message to the user.

How to use this list

Don't memorise 40 answers. Pick the 10 most relevant to the job, and for each one prepare a real example from your own work. If you haven't built anything yet, build one small RAG app with an evaluation set. It gives you a story for half the questions on this page.

Want to know what a specific company will ask? Paste the job description into our interview question predictor, and practise your answers out loud with the STAR method answer generator for the behavioral round.

Cluely charges $149.99/month to hide from screen share. We compared 6 AI interview copilots

Renekin AI — Tue, 06 Oct 2026 19:02:27 +0000

TL;DR

Most AI interview copilots sell a monthly subscription for something you need a handful of times a year.
The feature that defines the category, hiding from screen share, is often locked behind the most expensive plan. Cluely charges $149.99/month for it.
Ghost hides from screen share on every plan (even free), has no subscription, and costs about $2 per interview.

Here's how a job hunt actually goes: nothing for three weeks, then four interviews in five days, then silence, then an offer (hopefully). It's bursty. It's stressful. And almost every AI interview copilot on the market prices it like a Netflix subscription.

We build Ghost, so yes, we're biased. But every competitor price in this post comes from the vendor's own pricing page or widely published figures, and we link to them so you can check our homework. Let's talk about what you're really paying for.

AI interview copilots compared (October 2026)

Six tools, the cheapest way to use each one in a live interview, and the fine print.

Tool	What it costs	Hidden from screen share	Subscription	Free to try
Ghost by Renekin AI	≈ $2 per interview (packs from $10)	Yes, on every plan	No. Pay once, use when you need it	20 free credits, all features
Cluely	$19.99/mo, or $149.99/mo to hide from screen share	Only on the $149.99/mo plan	Yes	Limited, not hidden
Final Round AI	"$25/mo" (billed annually)	Pro plan only	Yes	No live sessions without Pro
LockedIn AI	$49.99/mo, or $9.99 per interview pass	Yes	Yes (or pricey passes)	A few trial credits
Parakeet AI	$29.50 for 3 hours, or $74.90/mo	Yes	Optional	Short trial
Interview Coder	$299/mo or $799 lifetime	Yes	Yes	Download only, no AI

Prices are the publicly listed USD prices as of October 4, 2026, and can vary by region and promotion. Sources are linked in each section below.

Cluely: the $130 "invisibility tax"

Cluely made a lot of noise with a launch that promised you could "cheat on everything." Bold. Then you open the pricing page.

Pro, $19.99/month: unlimited AI responses. Not hidden from screen share.
Pro + Undetectability, $149.99/month: the version that's actually hidden from screen share.

Read that again. The tool that promised you could cheat on everything wants an extra $130 a month to stay out of view. For an interview copilot, staying invisible isn't a premium add-on. It's the whole product.

Ghost hides from screen share on every plan, including the free one. We don't sell invisibility as an upgrade.

Fair's fair: if what you really want is an always-on meeting notetaker, Cluely does a lot there. But if you're searching for a Cluely alternative for interviews, the maths isn't close.

Final Round AI: "$25/month*"

That asterisk is doing a lot of work. Final Round AI's pricing page says "$25+/month." That's the annual rate, so roughly $300 up front. Pay month to month and reviewers report it's several times that. And live copilot sessions need an active Pro subscription; there is no free trial for them.

So the cheapest way to get through one live interview with Final Round AI is to commit to a year of interviews. We hope you're not that unlucky.

LockedIn AI: $9.99 for one interview

LockedIn AI's widely reported pricing is about $49.99 a month for unlimited, with a $9.99 pass for a single interview. There's also a lifetime plan reported at $1,499.25, which is a bold amount to spend on not having a job.

The pass is the honest option, and we respect that it exists. But Ghost's Best Value pack works out to around $2 per interview. That's roughly five interviews for the price of one LockedIn pass.

Parakeet AI: honest credits, expensive hours

Credit for Parakeet: they also skipped the forced subscription. Their credit packs start at $29.50 for 3 credits, and one credit is about an hour of live session. That's close to $10 an hour. The unlimited plan is reported at $74.90/month.

Same idea as Ghost, very different maths. With Ghost you pay per question, not per minute on the clock, so the long awkward "tell me about yourself" small talk doesn't cost you anything.

Interview Coder: $299 a month

No typo. Interview Coder's site lists $299/month or $799 lifetime, and the free download has no AI at all, so you can't try the part you'd be paying for.

For $299 you could buy Ghost's biggest pack ten times and still have enough left over for a celebration dinner after the offer.

How Ghost pricing works (the boring, fair version)

No subscription. No "annual billing" asterisk. You buy credits and spend them on what you actually use:

Text question: 1 credit
Screenshot (coding problems, slides, diagrams): 2 credits
Voice question (Ghost listens and answers): 3 credits

A typical interview uses about 50 to 60 credits. Packs are $10 for 200, $19 for 450 and $28 for 750. The biggest pack covers roughly 12 to 15 interviews, which is about $2 each. Every new account starts with 20 free credits, with every feature unlocked. See pricing.

What you get with Ghost

Hidden from screen share: Ghost's window is excluded from screen capture, so it doesn't appear in Zoom, Google Meet, Microsoft Teams or screen recordings. On every plan.
Hears the question for you: real-time transcription of the interviewer, with system audio captured when you're on headphones.
Reads your screen: screenshot a coding problem or a slide and get a structured answer in seconds.
Knows your story: add your resume and the job description, and answers sound like you, not like a textbook.
Panic button: an emergency hide hotkey and a disguised process name.
Always on top of every window, including fullscreen apps, on macOS 12+ and Windows 10+.

The verdict

If you don't mind a subscription, the big players will happily take your money every month. If you want an invisible AI interview copilot that works on the free plan, costs about $2 an interview and never auto-renews, there's one obvious answer.

Use it responsibly: practise with it in mock interviews, use it to organise your thoughts, and follow the rules of every interview you take.

Frequently asked questions

What is the best AI interview copilot in 2026?
If you want live answers that stay hidden from screen share without a monthly subscription, Ghost by Renekin AI is the best value. It works on macOS and Windows, hides from Zoom, Google Meet and Microsoft Teams on every plan, and costs about $2 per interview with credit packs from $10.

What is the best Cluely alternative?
Ghost. Cluely only hides from screen share on its Pro + Undetectability plan at $149.99 a month. Ghost hides from screen share on every plan, including the free one, and you pay once for credits instead of a subscription.

Is Ghost free?
Yes, you can start free. Every new account gets 20 free credits with every feature unlocked, including screen-share stealth. After that, credit packs start at $10 for 200 credits, and there is no subscription.

Does Ghost show up on screen share?
No. Ghost's window is excluded from screen capture on macOS and Windows, so it does not appear when you share your screen in Zoom, Google Meet or Microsoft Teams, or in screen recordings.

How much does an AI interview copilot cost?
Most charge a subscription: roughly $25 to $90 a month for Final Round AI depending on billing, $49.99 a month for LockedIn AI, $149.99 a month for Cluely with undetectability, and $299 a month for Interview Coder. Ghost is credit-based and works out to about $2 per interview.

Try Ghost free: 20 free credits, every feature unlocked, no card, no subscription. Download Ghost

Originally published at renekin.com.