Is the AI/ML engineering assistant free?

Zeplik is free to start and every new account includes credits. These skills run like any other Zeplik chat, so you spend only normal usage credits on the model you pick, with the cost shown before you send.

Can it write code, or just explain concepts?

Both. It designs architectures and explains trade-offs, and it drafts the code for the core path: retrieval, agent loops, eval scoring, serving configs. Treat the code as a reviewed draft, run it against your own tests, and verify versions before you ship.

Does it keep up with the latest models and frameworks?

It builds from the stack and versions you give it, and it is honest that the field moves faster than any training cutoff. It flags where a recommendation depends on a fast-moving library so you confirm it against current docs, rather than quoting a deprecated API as fact.

Can it help with RAG, agents, and evals specifically?

Yes, those are the core of it: RAG architecture and retrieval, agent loops with memory and guardrails, and evaluation harnesses with datasets and scoring you can wire into CI. It designs for production, not just a demo.

What do I need to give it?

A description of what you are building and your constraints is enough to start. For sharper work, paste your code, configs, model choices, and your latency, cost, and scale targets. The guidance is grounded in what you share.

Which models can I use?

Every frontier model Zeplik supports, in one chat: Claude, GPT, Gemini, and more. You can switch models mid-conversation and use the one best suited to the engineering task in front of you.

AI and ML engineering assistant

Describe your model, agent, or pipeline and get engineering work you can act on: a RAG architecture with retrieval and chunking, an eval harness that measures what matters, a serving and quantization plan that fits your latency and cost budget, a review of an agent loop before it hits production. Zeplik builds from your stack, data, and constraints, not a generic tutorial, and it works across every frontier model in one chat. It is honest about the line too: it drafts code and designs and tells you what to benchmark and verify before you ship.

See pricing

Who it is for

ML and LLM engineers building agents, RAG, and evaluation into real products
Backend and full-stack engineers adding AI features and needing the patterns
Founders and small teams standing up their first serving, eval, or vector stack
Researchers moving from a notebook to something that runs and is measured

How it works

Describe your system and constraints
Tell Zeplik what you are building, the model or models involved, your latency, cost, and scale targets, and the stack you are on, or paste the code and configs you already have. The work is grounded in your setup, so the guidance fits your constraints instead of a generic reference architecture.
Ask for the engineering work you need
Say it plainly, for example "design a RAG pipeline over these docs" or "set up an eval harness for this agent." Zeplik routes to the right skill without you naming it, and you can steer the model choice, the framework, the trade-offs, and the depth.
Review, benchmark, and ship
You get draft code, an architecture, or an eval plan with the reasoning shown, and you can iterate in the same conversation. Run it against your own evals, load, and security review, verify every library version and API against current docs, and confirm the cost and safety behavior before it goes live.

What you can ask

Real asks in plain language, and the concrete deliverable you get back. Open the assistant to try your own; nothing runs until you send it.

Design a RAG pipeline over the docs I'll describe: retrieval, chunking, and the model call.

An end-to-end architecture: a chunking and embedding strategy for your document type, a vector-store and retrieval design with the trade-offs called out, the prompt-assembly and model-call layer, and where to add reranking and evals, with draft code for the core path.

Set up an eval harness for my agent: what to measure, the dataset, and the scoring.

An evaluation plan matched to your task: the metrics that actually predict quality, how to build and hold out a dataset, an offline harness with scoring, and how to wire it into CI so a regression is caught before it ships, not after.

Recommend a serving and quantization setup to run this model under my latency and cost budget.

A serving design for your numbers: a quantization and precision choice with the accuracy trade-off stated, a serving-framework and batching recommendation, a hardware and memory estimate, and the levers to pull if you miss the budget.

Review this agent loop for reliability and what will break in production.

A hardening pass on the loop you paste: where it will loop, stall, or hallucinate a tool call, the retry, timeout, and guardrail gaps, the observability you are missing, and a prioritized list of fixes with the reasoning behind each.

Add memory to my agent so it remembers context across sessions.

A memory design for your agent: what to store, how to retrieve it, the short-term versus long-term split, the vector or store choice, and the write and eviction strategy, with the failure modes to test.

Explain the trade-offs between fine-tuning, RAG, and prompting for my use case.

A grounded decision for your task and data: when each approach wins, the cost and maintenance implications, how they combine, and a recommended path with the reasoning, so you are not guessing at the architecture.

What it can do

The assistant routes to the right skill for you. Each one below is a focused, ready-to-run workflow; follow a link to see exactly what it does.

LLM apps and agents

Design the systems that turn a model into a product. Where most AI engineering starts.

ML engineeringEnd-to-end guidance on building, training, and shipping machine-learning systems.
LLM app patternsThe proven patterns for RAG, tool use, and structured LLM applications.
Agent patternsReliable loops, planning, and tool-calling for autonomous and multi-step agents.
Agent memoryShort and long-term memory design so agents keep context across turns and sessions.
Structured outputConstrain model output to schemas and grammars for reliable, parseable results.

Evaluation, observability, and safety

Measure what you build and know when it regresses, before your users do.

EvaluationBuild eval datasets, harnesses, and scoring that catch regressions in CI.
ObservabilityTrace, log, and monitor LLM and agent behavior in production.
InterpretabilityProbe and understand what a model is actually doing inside.
Safety guardrailsInput and output guardrails, red-teaming, and abuse mitigation for LLM systems.

Serving and infrastructure

Run models efficiently, at your latency and cost, on the hardware you have.

ServingInference-server, batching, and deployment choices for latency and throughput.
QuantizationShrink and speed up models with the accuracy trade-offs made explicit.
Vector databasesChoose, index, and tune a vector store for retrieval at your scale.
Training frameworksFine-tune and train with the right framework, config, and distributed setup.

Models, modalities, and research

Go deeper on architectures, modalities, and the research side of the stack.

Model architecturesUnderstand and compare transformer and modern LLM architectures for your needs.
MultimodalDesign systems that combine text, image, audio, and video understanding.
Voice agentsBuild low-latency voice agents with speech-in, speech-out pipelines.
Voice AIThe speech-recognition, TTS, and streaming plumbing behind voice products.
Reinforcement learningThe RL libraries and patterns for training policies and RLHF.
Research engineeringTurn a paper or idea into a running, measured experiment.
Paper writingStructure and write up ML research clearly for review and publication.
Gemini CLIWork with the Gemini CLI and its agentic developer workflows.

What a general chatbot gets wrong on ML engineering, and what is different here

AI and ML engineering is where a general model is most likely to sound right and be subtly wrong, because the field moves fast and the model is answering from a training cutoff. Ask a raw chatbot how to serve a model and it will quote an API that changed, recommend a library version that is deprecated, or hand you an agent loop that has no retries and no evals. Zeplik grounds the work in your stack and is honest about what it cannot see.

The failure. A general model recommends a framework, API, or model version from its training cutoff, so the config it hands you is deprecated or the endpoint has changed.

How Zeplik grounds it. Zeplik builds around the stack and versions you tell it, and it flags where a recommendation depends on a fast-moving library so you verify it against current docs, rather than pasting a config that no longer runs.

The failure. Asked for an agent or RAG design, a general model hands you a happy-path loop with no retries, no timeouts, no guardrails, and no way to tell if it is working.

How Zeplik grounds it. The agent and app-pattern skills design for production from the start: retry and timeout behavior, guardrails, observability, and an eval harness, so you ship something you can measure and trust, not a demo that breaks on the second real input.

The failure. A general model quotes a benchmark, a latency number, or a cost figure with confidence, as if it had run it.

How Zeplik grounds it. Zeplik frames performance and cost numbers as estimates to validate against your own load, and it is upfront that it does not run your evals or benchmarks for you. It designs the measurement; you execute it on your real workload before you rely on the number.

What it does not do

Knowing the boundary is part of using it responsibly. This is where a person, not the assistant, has to own the call.

It drafts, you benchmark. Every architecture, config, and cost estimate is a starting point to validate against your own evals and load. It designs the system; it does not run your workload.
Verify fast-moving versions. Frameworks, model APIs, and libraries change monthly. Confirm every version, endpoint, and flag against current documentation before you depend on it.
Draft code is not tested code. Read it critically, run it against your own tests and security review, and confirm it behaves correctly in your environment before it ships.
Safety and cost are on you. Guardrails, rate limits, and cost controls have to be verified against your real traffic and abuse surface. Confirm the safety and spend behavior before you expose it.

This is engineering guidance and draft code for your review, not tested, production-ready ML or LLM systems. Model, framework, and infrastructure choices depend on your data, scale, and constraints, so benchmark them against your own evals and load, verify every library version and API against current docs, and confirm safety, privacy, and cost behavior before you ship.

Frequently asked questions

Is the AI/ML engineering assistant free?: Zeplik is free to start and every new account includes credits. These skills run like any other Zeplik chat, so you spend only normal usage credits on the model you pick, with the cost shown before you send.
Can it write code, or just explain concepts?: Both. It designs architectures and explains trade-offs, and it drafts the code for the core path: retrieval, agent loops, eval scoring, serving configs. Treat the code as a reviewed draft, run it against your own tests, and verify versions before you ship.
Does it keep up with the latest models and frameworks?: It builds from the stack and versions you give it, and it is honest that the field moves faster than any training cutoff. It flags where a recommendation depends on a fast-moving library so you confirm it against current docs, rather than quoting a deprecated API as fact.
Can it help with RAG, agents, and evals specifically?: Yes, those are the core of it: RAG architecture and retrieval, agent loops with memory and guardrails, and evaluation harnesses with datasets and scoring you can wire into CI. It designs for production, not just a demo.
What do I need to give it?: A description of what you are building and your constraints is enough to start. For sharper work, paste your code, configs, model choices, and your latency, cost, and scale targets. The guidance is grounded in what you share.
Which models can I use?: Every frontier model Zeplik supports, in one chat: Claude, GPT, Gemini, and more. You can switch models mid-conversation and use the one best suited to the engineering task in front of you.

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