There is a confident objection making the rounds in 2026 boardrooms: “Context windows are enormous now. We can just paste everything into a million-token model. We do not need a memory layer.” It sounds reasonable. Models from every major lab now advertise windows of a million tokens or more, enough to swallow a small library in one prompt89. If the model can read all of it, why bother building a company memory?
Because a context window and a company memory are not the same kind of thing, and no amount of growth in the first turns it into the second. A context window is the model’s short-term working memory for one request - the RAM of the machine3. A company memory is the persistent, governed, shared institutional knowledge your organisation runs on. Confusing the two is like buying more RAM and calling it a filing system. The RAM got bigger. You still have nowhere to keep anything.
This piece is for the CTO, head of data or operations lead being told a big enough window makes a memory layer redundant. It walks through what a context window actually is, why bigger is not better past a point, what you re-pay on every prompt, and the five things a window can never be. Then it lands the point that matters: the durable asset is a company memory - what we call a Company Brain - and the AI employees that do real work sit on top of it.
TL;DR
A context window is working memory, not a memory - it is per-session, stateless and reloaded from scratch on every prompt, like the RAM in a computer3.
Bigger is not better past a point - models use the start and end of a long context far better than the middle, so stuffing the window buries the fact you need1.
You re-pay on every prompt - input tokens are billed each call, and a one-million-token request can cost roughly 1,250 times a focused retrieval query and wait 30 to 60 seconds to start45.
A window can never be governed, shared, permission-aware or compounding - those live in a memory layer, not in the model’s input.
A Company Brain is the durable asset - persistent, governed company memory that survives turnover and improves with feedback, with AI employees working on top of it.
The Million-Token Temptation
The temptation is understandable. For two years the headline number in every model release has been the context window, and it has grown at a pace that makes “just paste it all in” feel inevitable. The problem is that the number answers a capacity question, and the thing companies actually need answered is a memory question.
- The windows really are huge - Gemini 2.5 Pro and Flash both expose about 1,048,576 tokens, GPT-5 tiers run from 400K up to around a million, and Claude offers 200K with a million-token beta89. On paper you can paste a lot.
- The demo is seductive - drop a 200-page contract into the window, ask a question, get a good answer. It works, so the leap to “do this for the whole company” feels small.
- The leap is where it breaks - one person pasting one document is not the same problem as thousands of people and AI employees needing governed, current, permission-aware answers across every system you run.
- The failure shows up later - Gartner predicts more than 40 percent of agentic AI projects will be cancelled by the end of 2027 on escalating costs, unclear value and weak risk controls - the exact failure modes of a paste-everything design12.
- The category error is the root cause - a bigger window makes a single call read more. It does nothing about persistence, governance, sharing or learning, which is what a company memory is for.
The Category Error
“Just use a bigger context window” treats an organisation’s knowledge as something you re-type into a text box for each question. A company’s memory is not text you carry to the model. It is an asset the model should be able to draw on - governed, shared and durable - without a human pasting it in every time. The window is where knowledge is used for one request. The memory is where it lives.
To see why the leap fails, you have to be precise about what a context window is - and what it is not.
What a Context Window Actually Is
A context window is the amount of text a language model can hold in view while it produces a single response. The clearest mental model, popularised by Andrej Karpathy, is that the model is the processor and the context window is its RAM: fast, temporary working memory that holds only what is relevant to the task in front of it right now314.
Three properties that never change, however big it gets
- It is per-session - the window belongs to one conversation. Nothing you place in it is visible to another user, another team, or an AI employee running a different task.
- It is ephemeral - when the session ends, the window is gone. The model does not carry a single token to the next request; it is stateless by default14.
- It is reloaded from scratch - the model does not “remember” the document you pasted yesterday. To use it again, you paste it again, and it is processed again, from zero.
None of those three properties improves when the window gets bigger. A one-million-token window is still per-session, still ephemeral, still reloaded from scratch. It just holds more before it is wiped.
| Property | Context window | Company memory |
|---|---|---|
| Lifespan | One session, then gone | Persists for years |
| Scope | One user, one task | Shared across people and AI employees |
| State | Stateless, reloaded each prompt | Accumulates and is retained |
| Governance | None - reads whatever is pasted | Permission-aware at the source |
| Learning | None - weights do not change | Improves with feedback |
Why the RAM analogy matters
No one buys more RAM and cancels their storage. RAM is where a program works; storage is where data lives between runs. Treating a context window as your company memory is the same mistake as running a business off RAM alone: everything you were working on vanishes at the next reboot, and every morning starts from an empty machine.
- RAM is for the task at hand - the window holds what one request needs, then clears.
- Storage is for what must survive - a company memory holds what the organisation must keep, retrieve and govern over time.
- You need both, doing different jobs - the model still uses a window for each call; the memory decides what goes into it and captures what comes back.
“Performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts.”
- Nelson F. Liu et al., Stanford University, “Lost in the Middle”1
Bigger Is Not Better: The Lost-in-the-Middle Problem
The pitch assumes a model reads a giant context evenly, so more context can only help. The research says otherwise. Language models attend to the edges of a long input far more reliably than the middle, so a bigger window can actually make the fact you need harder to reach.
- Position decides recall - Stanford’s widely cited study found accuracy is highest when the needed fact is near the start or end of the context and drops sharply when it sits in the middle, even for long-context models12.
- Effective context is smaller than advertised - reported testing suggests useful utilisation falls off well before the limit, with effective use dropping toward 60 percent beyond roughly 100K tokens4. Practitioners plan for 40 to 50 percent of the advertised window as the reliable zone8.
- Single-fact demos hide multi-fact reality - a model can hit 99.7 percent recall on a single “needle in a haystack” test yet average around 60 percent when several facts must be retrieved from a large context - a 40 percent silent miss rate4.
- More is not free accuracy - each extra token you paste is another place the important detail can hide. Beyond a point, adding context lowers the odds the model uses the right part of it.
- Precision beats volume - giving the model the few passages that matter reliably beats burying them in a million tokens of everything.
The Silent 40 Percent
A single-fact benchmark that reports 99.7 percent recall is not the workload you run. Real questions need several facts pulled from a large context, and there average recall can sit near 60 percent4. That is a 40 percent miss rate the model does not warn you about - it answers confidently from what it did surface. Volume of context is not the same as reliable use of it.
Stuff the Window vs Retrieve What Matters
Paste everything into a huge window
- ✗ Buries the answer - the fact you need can land in the weak middle1
- ✗ Silent misses - multi-fact recall drops with no warning4
- ✗ Slow and costly - every token is re-read and re-billed each prompt
- ✗ No governance - the window reads whatever is dumped in
Retrieve from a governed memory
- ✓ Answer in the strong zone - only relevant passages enter the window
- ✓ Higher reliable recall - fewer tokens, clearer signal
- ✓ Fast and cheap - thousands of tokens per query, not millions
- ✓ Permission-aware - retrieval respects who is asking
The Bill You Re-Pay on Every Prompt
Even if accuracy held, the economics do not. Input tokens are billed per request, and a context window remembers nothing between requests, so a paste-everything design pays the full cost of reading your knowledge again on every single prompt. That is the opposite of an asset - it is a recurring bill with no accrual.
- You pay for the whole window each call - a request that fills a one-million-token window has been reported to cost on the order of a couple of dollars in input alone, against a fraction of a cent for a focused retrieval query - roughly a 1,250-times difference per query46.
- You pay it again next time - because the window is stateless, the next question re-processes the same tokens from scratch. Ten questions about the same document means ten full re-reads14.
- Latency compounds the cost - large contexts have been reported to wait 30 to 60 seconds before the model even starts generating, tens of times slower than a retrieval pipeline45.
- Model choice multiplies it - filling the same window can cost about 0.15 dollars on one flash-tier model and 5 dollars on a premium one - the same information at a 33-times spread810.
- Retrieval flips the maths - sending a few thousand relevant tokens per query instead of the full corpus is where cost and latency collapse to something an operation can run thousands of times a day47.
Do the Math
Say a support team asks 5,000 questions a day against the same knowledge. Re-stuffing a large context on each one re-reads and re-bills that knowledge 5,000 times a day, every day. Retrieving only the relevant passages reads a few thousand tokens per query instead of the whole corpus4. The window design does not just cost more once - it costs more forever, because it never remembers what it already read.
| Dimension | Stuff a 1M-token window | Retrieve from memory |
|---|---|---|
| Tokens per query | Up to ~1,000,000 | A few thousand relevant ones |
| Relative cost per query | Reported ~1,250x higher4 | Baseline |
| Time to first token | Reported 30-60 seconds5 | Sub-second to a few seconds |
| Cost on repeat questions | Re-paid in full each time | Cheap, only the query changes |
| Scales to thousands/day | Painful on cost and latency | Designed for it |
Stop pasting. Start remembering.
Book a 30-minute call. We will map one process where a governed company memory beats re-stuffing context.
5 Things a Context Window Can Never Be
Cost and recall are the visible problems. The deeper point is structural: there are five things a company needs from its knowledge that a context window, at any size, simply cannot provide. Each of them has to live in a memory layer.
1. Persistent
- The window forgets - it holds nothing between sessions, so knowledge that matters tomorrow has to be stored somewhere that is not the window14.
- A memory retains - the decision made last quarter, the fix for a recurring fault, the reason a process exists stay available until they change.
2. Shared
- The window is private to one session - what one person pastes helps no one else and no AI employee running another task.
- A memory is common ground - every person and every AI employee draws on the same curated institutional truth, so answers are consistent across the company.
3. Governed and permission-aware
- The window has no access model - it reads whatever text is placed in it, with no concept of roles, residency or entitlement.
- A memory enforces permissions at the source - a request only retrieves what the person or AI employee asking is allowed to see, which is what auditors and the EU AI Act expect you to demonstrate16.
4. Compounding
- The window never learns - the weights do not change when you prompt, so the thousandth question starts as cold as the first14.
- A memory improves with feedback - corrections and confirmed answers are captured, so the recurring question gets a better answer over time instead of resetting13.
5. Survivable through turnover
- The window cannot hold institutional knowledge - it has nowhere to keep what your best people know, so when they leave, nothing in the window remembers it.
- A memory keeps know-how in the building - captured as work happens, the knowledge stays when the person who had it moves on.
The Test in One Sentence
Ask of any AI setup: when this session ends, when this person leaves, when a different team asks the same question tomorrow - does the company still have the answer? A context window says no to all three. A company memory says yes. That gap is the whole argument.
| What a company needs | Context window (any size) | Company memory |
|---|---|---|
| Persistence | No - wiped each session | Yes - retained over years |
| Sharing | No - private to one session | Yes - across people and AI employees |
| Governance | No - reads whatever is pasted | Yes - permission-aware at source |
| Compounding | No - no learning between calls | Yes - improves with feedback |
| Turnover survival | No - nothing to hold know-how | Yes - knowledge stays in the building |
Context Window vs RAG vs Company Memory
Once you separate “used for one request” from “stored for the company,” the three approaches people confuse fall into a clear stack. They are not competitors; they are different layers doing different jobs.
The three layers, plainly
- Context window - the model’s working memory for one call. You choose what to put in it; it forgets when the call ends3.
- Retrieval-augmented generation (RAG) - a technique that fetches the few relevant passages and places them in the window, so the model reads thousands of tokens instead of millions47.
- Company memory - the governed, persistent layer that decides what knowledge exists, who may see it, how it stays current, and captures what comes back. It uses retrieval as one mechanism, not as the whole story.
A bigger context window is an upgrade to the first layer only. RAG makes the first layer affordable and accurate at scale. A company memory is the layer that actually makes knowledge an asset rather than a paste job.
| Question | Big context window | Plain RAG | Company memory |
|---|---|---|---|
| Persists between sessions? | No | The index does; nothing new is captured | Yes, and it captures new knowledge |
| Scales past the window size? | No - hard limit | Yes - corpus can be huge | Yes - across every connected system |
| Enforces permissions? | No | Only if you build it in | Yes - by design, at the source |
| Improves with feedback? | No | No, unless extended | Yes - corrections are captured |
| Cost per repeat query | Full re-read each time | Low - only relevant tokens | Low - retrieval plus reuse |
Where This Nets Out
Long context, RAG and a company memory are not a fight to be won. The best systems route between them - a window for the call, retrieval to fill it precisely, and a governed memory that owns the knowledge and learns7. The mistake is thinking a bigger window removes the need for the layer above it. It removes nothing; it just makes one layer larger.
What a Company Memory Actually Is
A company memory - a Company Brain - is the organisation’s knowledge made persistent, governed and usable. It is the people-knowledge, processes and data that normally sit locked in separate systems and separate heads, connected so that any person or AI employee can ask a question and get a correct, sourced, permission-aware answer that survives when someone leaves.
What it is built from
- People-knowledge - the reasons, judgements and fixes that usually live only in experienced heads, captured as work happens so they stay when the person moves on.
- Processes - how the company actually does things, not a generic template, so answers reflect your operation.
- Data - the records in your email, Teams, SharePoint, CRM and ERP, connected in place rather than copied into yet another silo.
- Governance - permissions, residency and lineage applied at the source, so retrieval only ever returns what the asker is entitled to see16.
- Feedback - corrections and confirmations captured daily, so the memory gets more accurate over time instead of resetting each session13.
This is why the memory, not the model, is the durable asset. Models will keep changing; you will swap them as the market moves. Your memory - what your company knows and how it works - is the thing worth owning across all of them.
Paste-Everything Approach vs Company Memory
Paste everything into the window
- ✗ A human curates by hand - someone decides what to paste every time
- ✗ No memory of yesterday - each session starts blank
- ✗ No permissions - whatever is pasted is exposed
- ✗ Knowledge leaves with people - nothing is captured
Company memory (Company Brain)
- ✓ The memory supplies context - retrieval hands the model exactly what it needs
- ✓ Remembers across sessions - knowledge persists and compounds
- ✓ Permission-aware - answers respect who is asking
- ✓ Survives turnover - know-how stays in the building
“Most agentic AI projects right now are early stage experiments or proof of concepts that are mostly driven by hype and are often misapplied.”
- Anushree Verma, Senior Director Analyst at Gartner12
AI Employees Sit on Top of the Memory
The reason the memory layer is decisive is what it enables above it. AI employees - the ones that actually take routine work off people - only work if they can draw on persistent, governed, shared knowledge. A context window cannot give them that; a company memory can.
- They need to remember the customer - an AI employee handling an account has to know the last conversation, the open contract and the agreed terms, none of which fit the “paste it in each time” model.
- They need to respect permissions - an AI employee acting in your CRM or ERP must only see and touch what its role allows, which the memory enforces and the window cannot.
- They need to share a truth - two AI employees and a human working the same process must draw on the same institutional answer, not three private windows.
- They need to learn from feedback - the daily corrections that teach an AI employee your way of working are captured in the memory, so the whole team benefits, not one lost session13.
- They need continuity - work spans days and hand-offs; without a memory, an AI employee forgets the task the moment the session closes.
This is the Superkind order of operations: the Company Brain is the foundation, and AI employees connected to your real systems sit on top of it, learning your company through daily feedback. Get the memory right and the AI employees become useful. Skip it and you get a clever demo that forgets everything by tomorrow.
Why the Order Matters
An AI employee without a company memory is a temp on their first day, every day - capable, but starting from nothing each morning. An AI employee on top of a Company Brain is a colleague who remembers the customer, follows your process, respects who may see what, and gets better every week. Same model underneath. The memory is the difference.
How Superkind Fits
Superkind builds the Company Brain and the AI employees that sit on it. The starting point is your existing systems and the way your teams actually work, not a bigger text box you paste your company into.
- Company Brain as the foundation - your people-knowledge, processes and data connected into one persistent memory that anyone, human or AI employee, can query and that keeps knowledge when someone leaves.
- Sits on top of your stack - secure connectors into email, Teams, SharePoint, CRM and ERP. Nothing is ripped out or replaced; the knowledge is read where it already lives.
- Permission-aware by design - retrieval respects roles and entitlements at the source, so a person or AI employee only ever sees what they are allowed to.
- Model-agnostic - the memory is yours and the model is a choice; swap or upgrade the underlying model without losing your knowledge asset.
- Retrieval, not re-stuffing - the Company Brain hands the model the right, permission-aware context for each request instead of pasting the whole corpus, keeping cost and latency sane at scale.
- Learns from daily feedback - corrections and confirmed answers are captured so the memory compounds and the AI employees improve at your way of working13.
- AI employees do the routine work - once the memory is in place, AI employees take over routine tasks connected to your real systems, so you get more output without more headcount.
- Governance you can evidence - clear lineage of what knowledge exists, who may access it and where it came from, which is what the EU AI Act and auditors expect16.
| Approach | Bigger context window | Superkind Company Brain |
|---|---|---|
| Core job | Read more in one call | Remember, govern and share knowledge |
| Knowledge source | Whatever a human pastes in | Email, Teams, SharePoint, CRM, ERP in place |
| Persistence | None - wiped each session | Durable across years and staff changes |
| Permissions | None | Enforced at the source |
| Improvement | Static | Compounds with daily feedback |
Superkind
Pros
- ✓ Solves memory, not just reading - persistent, governed, shared knowledge
- ✓ Works on top of your systems - no rip-and-replace
- ✓ Model-agnostic - your asset outlives any one model
- ✓ Sane economics at scale - retrieval, not full re-reads
- ✓ Fast first result - one use case live in 8-12 weeks
Cons
- ✗ Not a self-serve text box - it needs engagement with our team
- ✗ Needs system access - we connect to where knowledge lives
- ✗ Depends on connectable sources - fully paper knowledge must be digitised first
- ✗ Overkill for a one-off - a single document analysis really can just use the window
Choosing Your Memory Layer
A bigger window is genuinely the right tool for some jobs. The point is not that long context is useless - it is that it is the wrong layer to treat as your company memory. Use these signals to decide what each job needs.
- Name the lifespan of the knowledge - if the answer must survive this session, it needs a memory, not a window.
- Name who needs it - if more than one person or AI employee must share the same answer, a private window will not do.
- Name the permissions - if different people may see different things, governance has to live in a memory layer.
- Name the repeat rate - if the same knowledge is queried many times a day, re-stuffing the window is a recurring bill you do not need.
- Name the learning - if answers should get better with feedback, only a memory compounds; the window resets.
- Name the turnover risk - if losing a key person would lose the knowledge, capture it into a memory before they go.
When a Big Context Window Is Fine on Its Own
- A one-off analysis of a single document or dataset
- The knowledge does not need to survive the session
- Only one person needs the answer, right now
- There are no permission distinctions to enforce
- The task will not be repeated at high volume
- Nothing about the answer needs to improve with feedback
| Signal | What it means | The right layer |
|---|---|---|
| Knowledge must persist | Answers are needed again tomorrow | Company memory |
| Many people and AI employees | A shared truth is required | Company memory |
| Different access rights | Governance must be enforced | Company memory |
| High query volume | Cost and latency matter | Retrieval into the window |
| One-off, one-person analysis | No persistence or sharing needed | Big context window is fine |
Building on a Window vs Building on a Memory
Betting on the window
- ✗ Locked to one model - your knowledge lives in a vendor’s input
- ✗ Costs grow with use - every prompt re-reads everything
- ✗ No governance story - hard to evidence for auditors16
- ✗ Fails at the demo-to-operation leap - a common cancellation cause12
Building on a memory
- ✓ Model-agnostic - swap models, keep your asset
- ✓ Costs stay flat with use - retrieval, not re-stuffing
- ✓ Governance you can show - lineage and permissions
- ✓ Compounds over time - the asset gets more valuable13
Frequently Asked Questions
A context window is the working memory a language model reads for a single request - like the RAM in a computer. It is per-session, ephemeral and reloaded from scratch every time you prompt the model, and it disappears when the session ends. A company memory is persistent institutional knowledge: your people-knowledge, processes and data connected into one governed store that survives staff turnover, is shared across every person and AI employee, and gets better with feedback. One is short-term scratch space you re-pay for on every prompt; the other is a durable asset the whole company owns.
Yes. A bigger window changes how much a model can read in one go, not whether that knowledge persists, is governed, or is shared. Even a one-million-token window is wiped at the end of the session, applies to one user, respects no permissions on its own, and is re-filled from scratch on the next request. Bigger context solves a capacity problem inside a single call. A company memory solves the persistence, governance and sharing problem across the whole organisation. They are different layers, and the second one does not go away because the first one grew.
You can, but it fails on cost, accuracy and governance the moment you do it at scale. You re-pay to process every token on every prompt, accuracy drops when the answer sits in the middle of a huge context, and there is no permission model deciding who is allowed to see what. It also learns nothing: the next question starts from a blank window and you paste everything again. Pasting works for a one-off analysis of a single document. It is not a memory for a company.
It is a well-documented finding that language models use the beginning and end of a long input far better than the middle. Stanford researchers showed that performance is highest when the relevant fact sits near the start or end of the context and degrades significantly when the model has to retrieve something buried in the middle, even for models explicitly built for long context. In practice it means stuffing a huge context window does not guarantee the model will actually use the fact you need - the more you paste, the more likely the important detail gets lost in the middle.
Yes, and you pay it repeatedly. Input tokens are billed per prompt, so a request that fills a one-million-token window costs far more than a focused query, and you re-pay that cost every single time because the window does not remember anything between calls. Long-context requests are also much slower, with reported waits of 30 to 60 seconds before a large context even starts generating. Retrieving a few thousand relevant tokens per query is dramatically cheaper and faster than re-reading the whole corpus on every prompt.
RAG fetches only the few passages relevant to a question and puts those into the context window, instead of pasting everything. It is cheaper and faster because the model reads thousands of tokens, not millions, and it scales to corpora far larger than any window. But plain RAG is still a retrieval technique, not a memory: it does not by itself decide governance, capture new knowledge from feedback, or share a curated institutional truth across people and AI employees. A company memory uses retrieval as one mechanism inside a larger, governed, persistent layer.
No. A context window is stateless: it does not carry anything from one session to the next, and the model weights do not change when you prompt it. Whatever you paste in is gone when the session ends, and the next conversation starts from zero. That is the opposite of institutional memory, which accumulates: a company memory captures corrections, decisions and know-how as they happen, so the answer to a recurring question gets better instead of resetting every morning.
Not on its own. A context window has no concept of roles, permissions or data residency; it simply reads whatever text is placed in it. If you paste a document into the window, anyone in that session sees whatever the model surfaces from it, regardless of whether they were entitled to the underlying record. A company memory enforces permissions at the source, so a request only ever retrieves what the person or AI employee asking is allowed to see. Governance has to live in the memory layer, because the window cannot provide it.
No, and conflating them causes most of the confusion. The model's knowledge is baked into its weights during training and is generic, frozen at a cutoff date, and identical for every customer. The context window is the fresh text you supply at request time. Your company-specific facts - this customer, this contract, this process - are never in the weights, so they have to come from somewhere at request time. A company memory is the durable, governed place that "somewhere" should be, rather than a human copy-pasting documents into the window by hand.
Because they solve a demo, not an operation. Gartner predicts over 40 percent of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls - exactly the failure modes of a paste-everything approach. A pilot that works when one person pastes one document rarely survives contact with real permissions, real cost at scale, real turnover and the need to keep knowledge current. Building on a persistent, governed memory from the start is what makes the difference between a proof of concept and a system people trust.
No, they are complementary. The language model provides reasoning and language ability; the company memory provides the persistent, governed, company-specific knowledge the model reasons over. You keep choosing and swapping the best model as the market moves, while the memory - your durable asset - stays with you. This model-agnostic split is why a memory layer is a safer long-term investment than betting your knowledge on one vendor's context window.
The Company Brain is the persistent, governed company memory. When a person or an AI employee asks a question, the Company Brain retrieves the right, permission-aware knowledge and hands the model exactly what it needs for that request - a well-chosen context, not a data dump. The window is used as short-term working memory for a single call; the Company Brain is the long-term institutional memory that decides what goes into it, captures what comes back, and improves with feedback. The model reasons; the Company Brain remembers.
The Act rewards systems where you can show governance, data lineage and access control. A governed company memory gives you a clear record of what knowledge exists, who may access it and where it came from, which is far easier to evidence than ad-hoc pasting of documents into a public model. Most knowledge-access use cases sit in the minimal or limited-risk tiers with light transparency duties, and SMEs get priority access to free regulatory sandboxes. Treating your knowledge as a governed asset from day one keeps you on the right side of the rules.
A focused first use case - one department, one process, the systems where its knowledge already lives - typically goes from assessment to production in 8 to 12 weeks. The early weeks map where knowledge lives and who may see it. The middle weeks connect the systems and tune the answers against real questions. The last weeks roll out to a team and measure against a baseline. You are not building a bigger window; you are connecting a memory the whole company can share, and first results usually show within 90 days.
Related Articles
- AI Agent Memory: Why Your Agents Keep Forgetting
- RAG vs Fine-Tuning: Which Fits Your Company Knowledge
- The Context Graph Behind a Company Brain
- Why a Company Brain Should Be Model-Agnostic
- Chat Knowledge Loss: What Disappears When the Session Ends
- Which LLM for the Mittelstand?
Sources
- Liu et al. (Stanford) - Lost in the Middle: How Language Models Use Long Contexts (arXiv 2307.03172)
- Lost in the Middle - Transactions of the ACL (MIT Press)
- MindStudio - Software 3.0: Why Karpathy Says the Context Window Is Your New RAM
- SitePoint - Long Context vs RAG: When 1M Token Windows Replace RAG
- DEV Community - 1 Million Token Context Windows Are a Trap. Here's Why
- KeepMyPrompts - 1M Context Windows Are a Trap: RAG vs Long Context Decision Framework
- TianPan - Long-Context Models vs RAG: A Production Decision Framework
- Adaptive Recall - Context Window Sizes: Every Major LLM Compared
- JuheAPI - Context Window Size Comparison: GPT-5 vs Claude 4 vs Gemini 2.5
- Curlscape - Google Gemini API Pricing Guide 2026
- Gartner - 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026
- Gartner - Over 40% of Agentic AI Projects Will Be Canceled by End of 2027
- mem0 - State of AI Agent Memory 2026: Benchmarks and Trends
- Atlan - Working Memory in LLMs: A Context Window Deep Dive
- Turing College - Context Engineering Guide 2025
- EU AI Act - Small Businesses Guide to the AI Act
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