What a Coaching Session Actually Looks Like

Sessions run 60 to 90 minutes, video by default, screen share when code or diagrams are in scope. The engineer sets the agenda in a one-paragraph note 24 hours before the call: what is the problem, what have they tried, where is the friction. Time is spent on the problem, not on warm-up. The coach pushes for specifics ("show me the prompt", "show me the eval", "show me the traces") and resists the urge to give abstract advice.

• Pre-session note: one paragraph, what is stuck, what was tried, what decision is open
• Live walk-through of the actual code, traces, or evaluation output, not slides
• Decision made or design sketched by end of session, captured in writing
• Action list for the next 1 to 2 weeks, scoped to be testable
• Async follow-up between sessions on a tight Slack or email channel for blocking questions
• Optional pair-programming block for hard debugging where the coach drives or watches

What Coaching Actually Covers

The topic mix shifts with the engineer's current work. The recurring themes below show up in nearly every engagement. None of them are exotic. All of them are the kind of pattern an engineer either learns by burning six months of production time or by spending an hour with someone who already burned that time.

• Design review of an in-progress AI feature: scope, retrieval strategy, prompt shape, tool surface, evaluation plan
• Architecture trade-offs: when to use an agent vs a chain, when to use a graph vs a router, when to add memory vs not
• Code review on agent and LLM application work, focusing on tool schemas, control flow, error handling, and idempotency
• Debugging hard-to-reproduce AI behavior: prompt regressions, model upgrades, non-determinism, context window collisions
• Evaluation strategy: what to measure, how to label, how to wire LLM-as-judge without lying to yourself
• Production readiness review before launch: budgets, fallbacks, observability, incident response, on-call shape
• Career and scope conversations specifically for engineers whose track is becoming AI-shaped: what to learn next, what role to aim at, when to specialize

Two Buyer Types: Engineer-Funded and Employer-Funded

Most coaches collapse the question of who pays into a single brochure. In practice the two engagements feel different, and the contract should reflect that. Engineer-funded engagements are quieter, more personal, and slightly cheaper per hour. Employer-funded engagements involve a manager in scoping, a budget that needs an invoice and a SOW, and sometimes outcomes language that ties to a performance cycle.

• Engineer-funded: month-to-month, sessions billed individually or in small packs, no manager involvement, confidential by default
• Engineer-funded use cases: out-of-pocket investment to accelerate a career shift into AI, second opinion on a hard architectural decision, an unbiased coach who is not also their boss
• Employer-funded: invoiced to the company, scoped against a written outcome, often paired with a quarterly review by the manager
• Employer-funded use cases: ramp a senior backend engineer into AI, retain a strong engineer who needs more challenge than the company can supply internally, fund a critical IC on a high-risk AI project
• Confidentiality model differs: in employer-funded engagements the manager sees objectives and progress, never the contents of the sessions
• Pricing reflects buyer: engineer-funded usually a 10 to 20 percent discount vs employer-funded, in exchange for tighter scope and direct billing

Why Coaching Beats a Course for Most Working Engineers

There are good courses on RAG, agents, evaluations, and LLM application engineering. They teach the canonical patterns and give the engineer a vocabulary. What they cannot do is look at the specific code in front of the engineer, recognize that the problem is a context-management failure dressed up as a prompt problem, and propose the specific change that fixes it. Coaching is the version of education that lives inside the engineer's real codebase, on the engineer's real schedule.

• Courses are general; coaching is specific to the engineer's actual stack and codebase
• Courses are paced by the curriculum; coaching is paced by what is in front of the engineer this week
• Courses are one-way; coaching pushes back on bad designs before they ship
• Courses are read once; coaching builds a pattern library the engineer carries to the next job
• A senior coach has shipped the patterns rather than only read them, which changes the kind of advice they can give

Cadence and Format

Most engagements settle into a weekly or bi-weekly rhythm with async support in between. The cadence is set by how often the engineer is making decisions worth talking through, not by a fixed schedule. During heavy build phases, weekly is correct. During steady-state operating phases, monthly with on-demand availability often works better. Long gaps without a session signal either fit issues or that the engagement should be paused.

• Weekly during active build or launch phases, 60 to 90 minutes per session
• Bi-weekly during steady operating or learning phases, with async support in between
• Monthly check-in pattern for engineers in maintenance mode or post-promotion who just need calibration
• Single-session option for one-off architecture or career decisions, no ongoing commitment
• Async channel between sessions: tight Slack DM, email thread, or shared doc; response within 24 hours on weekdays
• Pair-debugging blocks scheduled separately when the engineer needs hands-on help with a specific bug

Pricing

Public benchmarks across platforms like MentorCruise, IGotAnOffer, and direct senior-engineer coaching cluster in a narrow band for engineers with shipped AI experience. The numbers below reflect the senior end of the market in 2026.

• Single session, engineer-funded: $250 to $500 for 60 to 90 minutes
• Single session, employer-funded: $400 to $750 for the same time, invoiced to the company
• Monthly retainer, engineer-funded: $1,000 to $2,500 per month for 2 to 4 sessions and async support
• Monthly retainer, employer-funded: $2,000 to $5,000 per month, often with a quarterly written summary to the manager
• Team coaching: $5,000 to $12,000 per month for 3 to 6 engineers on the same team, with rotating individual sessions and a shared monthly review
• Red flag: anyone offering AI engineer coaching for under $100 per hour is a generalist career coach, not a senior practitioner

When Coaching Is the Wrong Answer

Coaching does not fix a structural problem at the company. If the team has no production AI work to coach against, no time to apply new patterns, no senior peers in the room, or a manager who refuses to let the engineer take architecture decisions, the coach becomes therapy. Some honest disqualifiers are listed below.

• The engineer is junior and needs general software craft, not AI-specific coaching
• The team has no AI features in production or planned within the next quarter
• The manager will not delegate architecture decisions to the engineer; coaching makes them sharper but with nowhere to apply it
• The company actually needs an architecture review or a fractional AI leader, not coaching for an individual
• The engineer wants someone to do their work for them rather than to challenge their thinking
• There is no budget for the engineer to run experiments, buy small amounts of model time, or attend the occasional conference

What Changes for the Engineer Over Six Months

A successful engagement produces visible deltas the engineer and their manager can both see. The changes below are typical for an engineer two to five years into AI work who arrives with strong fundamentals and a real backlog of ambiguous problems.

• Designs reviewed before they ship rather than retro-fixed in postmortems
• A working pattern library: when to use which agent topology, which retrieval shape, which evaluation method
• Production AI incidents drop in frequency or are resolved without escalation
• The engineer becomes the person their team brings hard AI questions to, rather than the person stuck on them
• Job market position improves: stronger story for senior or staff interviews, sharper portfolio, better-named projects
• Vendor and model-selection decisions stop being driven by Twitter and start being driven by their own evaluations

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