LLM Systems & Retrieval
RAG, search, context engineering, data pipelines, model routing, memory, and architectures for grounded AI applications.
Independent technical publication · Open to submissions
AI engineering, in practice.
AI Engineering Collective publishes technical deep dives, system breakdowns, applied guides, and informed analysis for people building and operating AI systems.
Enter the journalSystems · Infrastructure · Evaluation · Applied AI
The publication
A technical publication for the systems behind modern AI.
We publish work on AI architecture, retrieval, agents, evaluation, infrastructure, reliability, developer tools, AI products, and the engineering decisions that shape real systems.
The Collective is independent and open to builders, researchers, operators, product practitioners, technical leaders, educators, and first-time writers with useful experience to share.
AI engineering scope
The field, from model to production.
We cover the technical and organizational work required to design, ship, evaluate, and operate useful AI systems. These areas are anchors, not boundaries.
Agents & AI Applications
Agent design, tool use, orchestration, multimodal systems, workflow automation, and lessons from production applications.
Infrastructure & Operations
Inference, serving, observability, cost, latency, security, deployment, platform engineering, and operating AI at scale.
Evaluation, Reliability & Safety
Evals, testing, monitoring, guardrails, failure analysis, red teaming, quality systems, and responsible engineering practice.
Developer Tools & Workflows
Frameworks, SDKs, open-source projects, coding agents, engineering workflows, and the tools used to build AI products.
AI Products, Teams & Industry
Product strategy, technical leadership, team design, platform choices, market shifts, and clear analysis of the AI industry.
Strong work may cross several areas or establish a new one.
Editorial principles
Technical standards, open doors.
We judge the usefulness, rigor, and clarity of the work rather than the contributor's title, employer, audience, or academic affiliation.
Technical clarity
Complex systems explained precisely, with the assumptions, architecture, tradeoffs, and limits made visible.
Evidence
Claims grounded in tested experience, data, code, research, documentation, or clearly identified inference.
Practical value
Work that helps readers understand a system, make a decision, avoid a failure, or build something better.
Editorial independence
Transparent conflicts, honest attribution, fair analysis, and a clear distinction between insight and promotion.
An open collective
Built by the people building AI.
Useful AI engineering knowledge is widely distributed. We welcome work from industry, research, open source, startups, enterprises, classrooms, independent projects, and unconventional paths.
How to contributeContribute
Write from the work.
Send a finished draft or a focused pitch. We welcome clear, original work that teaches, explains, tests, compares, questions, or helps technical readers build and operate AI systems beyond the noise.
- Your idea A short summary, proposed title, central argument, intended reader, and what they will learn.
- Your material Your Medium profile, a draft link if available, and relevant sources, examples, demos, or repositories.
- Disclosure Tell us if the work appeared elsewhere and disclose relevant company, product, employer, client, or financial interests.
- Editorial review Submission does not guarantee publication. Accepted work may be edited, or returned for revision.