What is context engineering, and what changed in 2026?
Context engineering is the design of everything an AI system reads before it answers — system prompts, skills, memory files, reference documents. In July 2026 Anthropic removed over 80% of Claude Code’s system prompt for its newest models “with no measurable loss on our coding evaluations,” because newer models infer from surrounding context what older models needed spelled out.
For business AI the lesson is not “write less.” It’s to delete the scaffolding older models required while keeping what the model cannot possibly derive: your brand voice, your pricing floors, your sign-off authority, and your confidentiality boundaries.
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The number that just repriced a lot of work
On July 24, 2026, Anthropic published a post about how it prompts its own coding tool, and buried in the second section was a number that should make anyone who spent last year building AI instructions sit up:
“We removed over 80% of Claude Code’s system prompt for models like Claude Opus 5 and Claude Fable 5 with no measurable loss on our coding evaluations.”
Read that carefully, because the precision matters. Not “we simplified our prompt.” Not “we cleaned things up.” They deleted four fifths of the instructions governing their flagship developer product and their evaluations couldn’t detect the difference. The scaffolding wasn’t helping any more. It had become weight.
If you have spent the last eighteen months building an AI capability inside your company, you have almost certainly accumulated the same kind of weight. The long system prompt. The document of rules about tone. The list of things the AI keeps getting wrong, appended to over months. The reminders repeated in three places because it kept ignoring them in one. That work was rational — those fences existed because older models genuinely needed them. What Anthropic has now demonstrated, on their own product, with their own evaluations, is that a large portion of it was compensating for a limitation that no longer exists.
That is a real and uncomfortable form of depreciation. Heavily-instructed AI configurations were treated as a moat through 2025 — proprietary prompt libraries, carefully-tuned instruction sets, the internal wiki of what to tell the AI. A meaningful share of that asset just lost value, and it lost value through no fault of the people who built it.
The post was written by Thariq Shihipar, a member of technical staff at Anthropic, who announced it on X the same morning:
We removed ~80% of the Claude Code system prompt for our newest models, this is what we’ve learned about writing system prompts, skills and Claude.MDs for them.
One clarification worth making up front, because it affects how much weight to put on this: the X post and the blog post are the same person publishing the same piece on two surfaces. This is one well-placed engineer’s account of one product’s evolution, not two independent confirmations of a general law. It is credible precisely because it’s specific and self-reported, including the parts that are unflattering.
And there is a second, larger caveat that shapes everything below. Anthropic’s post is about Claude Code and coding agents. Every example in it is code: comment density, tool parameters, test suites, plan-mode markdown, API-design rubrics. It says almost nothing about the kind of AI work most companies are actually doing — drafting the proposal, answering the client, deciding what gets escalated. Extending its lessons to business AI is our argument, not theirs. That distinction is the whole reason this article exists, and we’ll be explicit every time we cross the line.
The six shifts, in Anthropic’s own words
Anthropic frames these not as new rules but as corrections — “a number of previous context engineering best practices that had become myths.” Their words, verbatim:
| Then | Now |
|---|---|
| Give Claude rules | Let Claude use judgement |
| Give Claude examples | Design interfaces |
| Put it all upfront | Use progressive disclosure |
| Repeat yourself | Simple tool descriptions |
| Memory in CLAUDE.md files | Auto-memory |
| Simple specs | Rich references |
The reasoning behind each one is more useful than the headline.
Rules gave way to judgment because strong guidance was always a trade. Anthropic’s old system prompt said “In code: default to writing no comments. Never write multi-paragraph docstrings or multi-line comment blocks — one short line max.” They now describe why they accepted that: “without these guardrails for older models, the comments Claude wrote would be incorrect in many cases and we had to accept this tradeoff.” The rule was never right. It was less wrong than the alternative.
Examples gave way to interface design because examples turned out to constrain rather than teach: “giving examples actually constrains them to a certain exploration space.” The replacement is to make the tool itself more expressive — their example is a to-do tool where simply listing status as an enumeration of pending, in_progress, and completed communicates the intended usage without a worked example.
Front-loading gave way to progressive disclosure — loading the right context at the right time rather than everything at once. They moved code review and verification out of the system prompt and into skills that get called only when relevant. They also apply it to tools: some tool definitions are now “deferred loading,” meaning the agent has to go look them up before use, so they cost nothing until needed.
Repetition gave way to simple tool descriptions. Worth noting this shift is narrower than it first sounds. Older models “could sometimes need repeated instructions or be more likely to listen to instructions at the end of their context window than at the start” — so instructions got duplicated between the system prompt and the tool description. The fix is specific: put the instruction in the tool description, once.
Manual memory gave way to automatic memory. The # hotkey that wrote to a CLAUDE.md file has been superseded: “Claude now automatically saves memories that are relevant to the work and to you.”
Simple specs gave way to rich references. A plain markdown spec is often a lossy description of something that could be shown precisely instead — an HTML artifact, a function in another codebase, a test suite, a rubric that a verifier agent can apply.
Alongside the post, Anthropic shipped a command that does the cleanup for you: /doctor in Claude Code, which they describe as a way “to rightsize your skills, and CLAUDE.md files.” If you work in Claude Code, run it. It’s the most concrete takeaway in the whole announcement.
One diagnosis in the post deserves more attention than it’s getting, because it’s the failure mode most companies have and can’t see. Anthropic read transcripts of their own internal usage and found “several conflicting messages in a single request like ‘leave documentation as appropriate,’ or ‘DO NOT add comments’ as our system prompt, skills, and user requests clash with each other.” Not a wrong instruction. Contradictory instructions, arriving from three different layers, in the same request. Claude can usually resolve it — but, as they put it, “Claude must think more carefully about these overlapping and conflicting messages before deciding what to do.”
That is a tax you pay on every single request, and it is invisible unless someone goes looking.
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Context engineering versus prompt engineering
Since the term is doing a lot of work here, it’s worth being precise about it — especially because most of the writing on context engineering is aimed at engineers building agents, not at leaders running companies.
Anthropic defined it in a companion piece back in September 2025: context engineering is “the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference, including all the other information that may land there outside of the prompts.” They position it as a succession rather than a rivalry: “At Anthropic, we view context engineering as the natural progression of prompt engineering.”
The practical distinction, in business terms:
Prompt engineering is what you type. It’s per-request, disposable, and it belongs to the person doing the task. If your marketing manager gets better at asking, that’s prompt engineering.
Context engineering is what the system already knows before anyone types anything. Your standards, your voice, your history, your definitions of done, your boundaries — plus the machinery that decides which of those load for which task. It’s persistent, shared, and it belongs to the company.
That’s why the distinction matters commercially and not just technically. Prompt skill walks out the door with the person who has it. Context is an asset that stays. We’ve argued before that your team doesn’t need to learn to prompt so much as it needs to define what “good” looks like — and a definition of good is context, not prompting.
Which sets up the trap in this week’s news. If context is the company asset, and Anthropic just deleted 80% of theirs, the obvious inference is that the asset was overvalued. That inference is wrong, and the reason is the most useful thing in the post.
Why “write less” is the wrong lesson for business AI
Here is the sentence in Anthropic’s post that tells you exactly when deleting instructions is safe. It’s the replacement instruction they wrote after removing the rule about comments:
“Write code that reads like the surrounding code: match its comment density, naming, and idiom.”
Look at what that instruction actually does. It doesn’t tell the model what good looks like. It tells the model where to find out. The answer is already sitting in the codebase — the conventions are on the screen, in the files, in every function around the one being edited. The model isn’t exercising taste. It’s reading local convention and conforming to it.
That’s the mechanism. “Rules → judgment” works where the right answer is derivable from the surrounding context. In code, it very often is. And code has a second property that makes the trade even safer: it has compilers and test suites. When the model guesses wrong, something goes red. Wrong answers announce themselves, immediately, to the person who caused them.
Now apply the same test to the work your company actually runs on.
Your brand voice is not derivable from a logistics brief. Nothing in the surrounding documents tells the model that you never use exclamation marks, that you refuse to denigrate a competitor, that your register is direct rather than warm. Your pricing floor is not inferable from a proposal — the proposal shows what you charged, not the number below which you walk away. Who has authority to sign off on what. What “done” means for a deliverable in your firm. Which client facts must never appear in which room. None of it is in the surrounding context.
And here’s the part that should genuinely worry you: none of it fails loudly. There is no compiler for tone. No test suite goes red when the AI quotes a price below your floor, adopts a competitor’s framing, drafts something that reads like everyone else, or carries a detail from one client’s file into another client’s document. The output looks fine. It’s confident, well-formatted, and plausible. You find out weeks later, from a client, or you never find out at all.
Two of those four questions are the ones people skip. The failure-visibility question is the one that separates coding advice from business advice, and the cost question is what turns this from a tidiness exercise into a risk decision.
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Take the readiness read →Strip the scaffolding. Keep the non-derivable.
That’s the whole reframe, and it’s more demanding than “write less” because it requires you to sort rather than to cut.
Scaffolding is everything that exists because a model used to be weaker. The workaround for the thing it kept getting wrong. The reminder repeated in three files because it ignored the first two. The rigid format rule that stops it producing something clumsy. The fence you built after a bad output in March. Every one of these is a candidate for deletion, and — this is Anthropic’s real contribution — you should test the deletion rather than assume the fence is still needed. Most of these were written against a model that no longer exists.
Non-derivable is everything the model cannot work out from what’s in front of it, no matter how capable it becomes. Your voice. Your judgment boundaries. Money — floors, discount authority, what needs a second signature. Confidentiality — which information crosses which wall. Sign-off — who decides, and at what point. Definitions of done. This is not scaffolding. This is your business, written down.
Strip the scaffolding. Keep the non-derivable. The first category is depreciating. The second is the entire asset — and no model release will ever hand it to you.
The distinction also resolves what looks like a contradiction in Anthropic’s own guidance. They tell you to keep files lightweight and to avoid overconstraining. But on skills, they add a qualifier that almost every write-up of this post has dropped:
“Avoid making them overconstrained, except in highly important areas.” Anthropic drew the line themselves — they just didn’t develop it, because their domain is code, and in code the important areas are narrow. In a company, the important areas are most of what makes the company yours.
That exemption is the hinge. We are not arguing against Anthropic here. We are taking a caveat they wrote in passing and pointing out that for a business it isn’t a footnote — it’s the main body of the work. Their post is about a codebase, where the non-derivable slice is thin. Your company is mostly non-derivable slice.
The differentiator just moved up-stack
Here’s the second-order consequence, and it’s the one worth taking into a budget conversation.
If the model now supplies more of the judgment, then the differentiated portion of any company’s AI stops being the instructions and becomes the architecture around them. Which knowledge loads for which task. Where the human gate sits, and what it’s allowed to stop. What happens to a correction — whether it evaporates in a chat window or gets written back somewhere it will apply next time. How a new capability gets added without contradicting three existing ones.
None of that arrives in a model update. It’s design work, and it’s specific to your company.
This is why the depreciation is uneven, and why it’s worth being precise about what lost value this week. A prompt library is a collection of instructions — the more of it was scaffolding, the more of it just got marked down. An operating system is a set of decisions about structure, boundaries, and learning. A model release makes the first one obsolete. It makes the second one more valuable, because the differentiator moved to the layer the platform doesn’t supply. When the floor rises, what’s above the floor is what counts.
It’s the same reason more agents keeps making companies less intelligent rather than more: accumulation was never the mechanism. Structure was. Anthropic’s post is, read one way, a very senior engineer arriving at that conclusion from the inside — they didn’t get better results by adding to their instructions, they got them by removing what shouldn’t have been there and moving the rest into a structure that loads on demand.
For our clients, this part was Tuesday. Progressive disclosure — knowledge held in separate files that load only when the task calls for them — has been how we build since well before it had a name attached to it; we wrote it up when Anthropic first told developers to stop building agents and build skills back in April. That’s not a claim to be ahead of anyone. It’s the ordinary experience of building on a platform: the vendor eventually formalizes the pattern you adopted because the platform made it obvious.
What’s genuinely new in this post isn’t progressive disclosure. It’s the empirical permission to delete — with a number attached, from a team willing to publish it about their own flagship product.
What we found when we ran the audit on ourselves
We run our own firm on the kind of AI operating system we build for clients, which means this post was an invitation to audit our own house. We did, the same day. It found real things.
Roughly 1,500 lines of context were loading before any work started. Not per project — every session, regardless of task. A session about a client proposal was loading the same material as a session about our accounting. By Anthropic’s diagnosis, most of that should be a tree of files that load when relevant rather than a block that loads always.
Two memory systems were quietly overlapping. Automatic memory has been accumulating durable facts in one place; our own hand-maintained context files have been accumulating them in another. Nobody designed that overlap — it’s exactly the drift Anthropic describes when they note that Claude “used to rely on CLAUDE.md as a source of memory” and now “we have memory, artifacts, and skills.” When a new capability arrives, the old structure doesn’t remove itself. You end up with two systems holding overlapping truth and no rule about which wins.
Two append-only logs had grown to about 1,100 lines each with no pruning criterion. One holds hard-won technical lessons, the other operational failures. Both are genuinely valuable and both were designed to only ever grow. Nobody had ever written down the rule for when something leaves.
Now the part that matters more than the findings.
We are not touching the critical guardrails. The secrets protocol stays. The rule that nothing gets published without a human review stays. Client-confidentiality boundaries stay. Money and sign-off rules stay. Every one of these is verbose, some are repeated in more than one place, and a tidiness pass would flag them all.
They stay for the reason the derivability test gives: their failure modes are silent and expensive. And they stay with Anthropic’s own blessing — “except in highly important areas.” If a simplification pass deletes the thing that stops you leaking a credential, you didn’t rightsize your context. You removed a control and called it hygiene.
That’s the honest version of this exercise. Deleting instructions feels productive. Roughly half of what we found was genuinely dead weight from a weaker model. The other half was load-bearing and looked identical from the outside. The work is the sorting, and the sorting is not automatable — which is precisely why /doctor is a genuine help for a codebase and only a starting point for a company.
Where outside help actually fits
We should be straight about which parts of this you can do yourself, because most of it you can.
The deletion pass is genuinely self-serve. Finding the contradictions, spotting the repeated reminders, testing whether a fence is still needed — that’s an afternoon with the prompt below and someone who knows the business. Don’t hire anyone for that.
Three things are hard from the inside, and they’re hard for structural reasons rather than technical ones.
The first is that naming the non-derivable is political. Writing down your pricing floor means someone has to decide what it is. Writing down sign-off authority means saying out loud who doesn’t have it. These documents get vague at exactly the points where the organization hasn’t settled the question, and the vagueness is invisible to the people who share the assumption. An outsider asking “so who actually decides this?” is doing something an insider usually can’t.
The second is pattern library. Knowing which instructions were scaffolding and which are load-bearing is a judgment that improves with having seen it go wrong in a dozen other companies. It’s the difference between guessing and recognizing.
The third is the cadence. A one-time cleanup decays. What keeps context healthy is a standing loop — corrections flowing back into the system, a periodic review with a real owner. That’s what makes an AI system that gets smarter every time you use it rather than one that quietly rots. Most companies can design that loop; far fewer sustain it without an outside accountability rhythm for the first few months.
That’s the work behind an AI operating system your company actually owns — context design, structure, governance gates, learning loops, running in your accounts rather than ours. That engagement is what we call CompanyOS. And whether you do it with us or with anyone else, apply the same test you’d apply to any firm: ask what you own at the end. If the answer is a system your team can operate, extend, and audit without the firm that built it, that’s an architecture engagement. If the answer is a dependency, the model release that just devalued prompt libraries will devalue that too.
What to do in the next 90 days
None of this requires an engineer, and none of it requires waiting for a project.
Weeks 1–2: find the contradictions. Gather every place you’ve written instructions for your AI — system prompts, project instructions, custom GPT configs, context documents, the shared doc of tips. Put them side by side and look for the pattern Anthropic found in their own transcripts: two instructions that can’t both be followed. Every contradiction is a tax on every request, and resolving one is pure gain with no risk.
Weeks 2–4: sort, don’t cut. Run the derivability test on each surviving instruction. Two piles: could the AI work this out from the material in front of it, and does a wrong answer announce itself? Both yes, it’s scaffolding — mark it for deletion. Either no, it’s load-bearing — keep it and make it clearer. Expect the split to surprise you in both directions.
Weeks 3–5: write down the non-derivable properly. This is the real work, and it’s a business exercise rather than an AI one. Your voice, with examples of what you’d never say. Your money boundaries — floors, discount authority, what needs a second signature. Your confidentiality walls. Your escalation paths. Your definitions of done. If your firm has never written these down, that’s the finding: your AI has been guessing at them, and so, probably, have some of your people.
Weeks 4–8: restructure into a tree. Take what survived and stop loading it all at once. One short always-on file describing what the company is and the handful of gotchas that apply everywhere, then separate files per domain that load when the task calls for them. This is Anthropic’s progressive disclosure applied to a company instead of a codebase, and it’s the same structural insight behind treating your company’s knowledge as a compiled asset rather than a pile of documents.
Weeks 6–12: build the loop and name an owner. Decide where corrections go when someone spots a bad output — which file gets edited, by whom, reviewed how often. Give the whole context layer a named owner with time allocated. Without this step, everything above decays back to where it started within two quarters. A context layer with no owner is a document; a context layer with a loop is a system that compounds.
And re-run the sort on every major model release. That’s the durable lesson from this week. Some fraction of your instructions is compensating for limitations that will quietly disappear. If you never re-test, you’ll carry that weight for years and mistake it for architecture.
Audit Your AI Context in One Sitting
Paste this into your AI assistant along with whatever instructions, prompts, and context documents you’ve accumulated. It runs the derivability test on your own material and gives you a sorted list — what to delete, what to keep, and what you’ve never written down.
Context: I’m auditing the instructions and context we give our AI systems. I’m going to paste our current material — system prompts, project instructions, context documents, house style notes, and any running list of “things to tell the AI.” We are a [INDUSTRY] company with [NUMBER] employees. Ask me clarifying questions before each step if you need them.
Step 1 — Find the contradictions: Read everything I’ve pasted and identify any instructions that conflict with each other, including soft conflicts where two rules pull in different directions in a plausible situation. For each, quote both instructions, explain the situation where they collide, and propose a single replacement.
Step 2 — Run the derivability test: For every instruction, answer two questions. (a) Could you work out the right answer from the surrounding material in a typical task, without this instruction? (b) If you got it wrong, would anything automatically catch it — a test, a checker, a required review — or would the output just look fine? Sort into SCAFFOLDING (yes to both) and LOAD-BEARING (no to either), and show your reasoning for anything close to the line.
Step 3 — Flag what’s missing: Based on what a company like mine needs, list the non-derivable things I have NOT written down anywhere. Look specifically for: brand voice with examples of what we’d never say, pricing floors and discount authority, who signs off on what, confidentiality boundaries between clients or departments, definitions of “done,” and escalation paths. Ask me for each one you can’t find.
Step 4 — Propose the tree: Design a progressive-disclosure structure: one short always-loaded file (what the company is, plus gotchas that apply everywhere), then domain files that load only for relevant tasks. Tell me which of my current material goes where, and what should simply be deleted.
Output: A sorted audit — contradictions to resolve, a delete list, a keep-and-sharpen list, a written-nowhere list I need to answer myself, and a proposed file structure with a suggested owner for each file.
This gets you the sort. What it can’t do from the outside is settle the questions in the “written nowhere” list — those are decisions about your business, and they’re usually vague precisely where the organization hasn’t agreed yet. That’s the readiness question underneath all of this. See where you stand →
Sources
- The new rules of context engineering for Claude 5 generation models — Thariq Shihipar, Anthropic, July 24, 2026 (over 80% of Claude Code’s system prompt removed with no measurable loss on coding evaluations; the six shifts; conflicting-instruction diagnosis; “except in highly important areas”;
/doctor) - Thariq Shihipar on X — July 24, 2026 (“We removed ~80% of the Claude Code system prompt for our newest models” — the announcement post; same author as the blog post above)
- Effective context engineering for AI agents — Anthropic, September 29, 2025 (definition of context engineering; “the natural progression of prompt engineering”)
- Building verification loops in Claude Code with skills — Anthropic, July 22, 2026 (companion post on moving verification into callable skills)
- The rise of “context engineering” — LangChain, June 23, 2025 (dynamic-systems framing; write/select/compress/isolate strategies)
- What Is Context Engineering? — IBM, 2026 (context selection, structuring, compression, sequencing as discrete steps)
- The New Skill in AI is Not Prompting, It’s Context Engineering — Philipp Schmid, June 30, 2025 (the right information, in the right format, at the right time)
- Context Engineering Guide — DAIR.AI Prompt Engineering Guide, 2026 (component breakdown: system prompt, instructions, tools, RAG and memory, state)
- What is context engineering in AI agents? A practical guide — Neo4j, December 19, 2025 (selecting, structuring, and delivering facts per step)
- Context engineering vs. prompt engineering — Elastic, January 20, 2026 (how you communicate versus what the model can access)
- Context engineering vs prompt engineering: the difference — Redis, June 23, 2026 (instruction versus knowledge-at-answer-time)
- Stop Building Agents. Build Skills. — bosio.digital, April 27, 2026 (progressive disclosure and skills architecture, three months before this post)
Frequently Asked Questions
What is context engineering?
Context engineering is the practice of designing everything an AI system reads before it responds — system prompts, skills, memory, reference files, and the rules that decide which of those load when. Anthropic defines it as “the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference, including all the other information that may land there outside of the prompts.” In business terms: prompting is what an individual types, context engineering is what the company has already taught the system.
What's the difference between context engineering and prompt engineering?
Prompt engineering is per-request and belongs to the person doing the task; context engineering is persistent and belongs to the company. Anthropic frames context engineering as “the natural progression of prompt engineering” rather than a replacement. The commercial difference matters: prompt skill leaves when the person leaves, while a well-designed context layer is an asset that stays and compounds.
Did Anthropic really delete 80% of its system prompt?
Yes, with a specific qualifier worth keeping. Anthropic states: “We removed over 80% of Claude Code’s system prompt for models like Claude Opus 5 and Claude Fable 5 with no measurable loss on our coding evaluations.” That’s one product — Claude Code — measured on coding evaluations. It’s strong evidence that instruction-heavy configurations built for older models carry removable weight, and it is not a claim that 80% of any company’s AI instructions are useless.
Is prompt engineering dead?
No. What’s changed is where the real gains sit. Individual prompting still matters for individual work, but the durable advantage has moved to context and architecture — what the system knows before anyone types, and how that knowledge is structured. Anthropic’s own framing is succession rather than obsolescence: context engineering is the next stage of the same discipline, not its replacement.
Should we delete our AI instructions too?
Sort them before you delete anything. Use the derivability test: could the AI work out the right answer from the material already in front of it, and would a wrong answer be caught automatically? If both are yes, it’s scaffolding from a weaker model and you should test removing it. If either is no — as with brand voice, pricing floors, or confidentiality rules, where nothing goes red when the model guesses wrong — keep it. Anthropic makes the same carve-out for skills: “Avoid making them overconstrained, except in highly important areas.”
What should stay in a CLAUDE.md or company context file?
Anthropic’s guidance for codebases is to “keep your CLAUDE.md lightweight and briefly describe what your repo is for, but spend most of the tokens on gotchas,” and to avoid stating the obvious things the model could see for itself. The business equivalent: a short always-on file covering what the company is and the handful of rules that apply everywhere, with everything domain-specific moved into separate files that load only when relevant. They also name treating these files as “a central repository for every known practice” as a common myth.
How do I see or change the Claude Code system prompt?
You generally shouldn’t need to. Anthropic’s guidance is explicit that for Claude Code “you will likely never modify this” — the system prompt is tied to the product, and the parts you actually control are your CLAUDE.md files, skills, and references instead. What they do recommend is running the /doctor command, which they built “to rightsize your skills, and CLAUDE.md files.” If you’re building your own agent rather than using Claude Code, the system prompt is where they say you should spend real time.
What are context engineering best practices for a business rather than a codebase?
Four things, in order. Resolve contradictions across layers first — they tax every request and cost nothing to fix. Sort remaining instructions into scaffolding and non-derivable using the failure-visibility test. Write down the non-derivable properly: voice, money boundaries, sign-off authority, confidentiality walls, definitions of done. Then restructure into progressive disclosure — a light always-on layer plus domain files that load on demand — and give it an owner with a correction loop. The coding-specific advice in Anthropic’s post doesn’t transfer directly, because code has compilers and business judgment doesn’t.
Does this make our AI investment obsolete?
It depreciates one part and appreciates another. Instruction libraries built to compensate for weaker models lose value with each capable release. Architecture — which knowledge loads when, where the human gate sits, what a correction teaches the system — gains value, because as models supply more judgment the differentiator moves up-stack to the layer the platform doesn’t provide. The practical implication: stop investing in accumulating instructions, keep investing in structure and in writing down what only you know.
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