Inside the AI Machine: Vectors, Attention & RAG
Syed Ahmer Shah
"There is no ghost in the machine. There is only linear algebra, wearing a very convincing costume."
Every time someone calls an AI system "smart," a mathematician somewhere sighs quietly into their coffee. Not because it's wrong exactly — but because the truth is both less mystical and more impressive. There is no brain in a data center. There is no understanding in the human sense. What exists is an obscene amount of arithmetic, arranged with enough care that it starts producing outputs that look like thought. This article opens the hood. No metaphors about "digital consciousness," no science-fiction hand-waving — just the actual anatomy: the vectors, the matrices, the pipelines, and the very real, very current controversies around how these systems are built, trained, and occasionally leaked onto the public internet.
1. What Is AI, Really?
1.1 A Working Definition
Artificial Intelligence, stripped of marketing language, is the field of building systems that perform tasks which normally require human cognition — recognizing patterns, generating language, making predictions, or acting on incomplete information. Modern AI, and specifically the kind that writes your emails and argues with you about semicolons, is built almost entirely on a subfield called machine learning (ML), and within that, a further subfield called deep learning.
The distinction matters:
Classical AI (1950s–1980s) relied on hand-coded rules — if X, then Y logic trees written by humans. This is often called "symbolic AI" or "Good Old-Fashioned AI" (GOFAI).
Machine Learning flips this: instead of writing the rules, you show the system enormous amounts of data and let it derive the rules statistically.
Deep Learning is machine learning using multi-layered artificial neural networks — the "deep" refers to the number of layers, not the profundity of the output (a distinction the industry conveniently forgets when writing marketing copy).
1.2 A Brief History — From Dartmouth to Deep Learning
The term "Artificial Intelligence" was coined in 1956 at the Dartmouth Summer Research Project, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The proposal, with almost comic optimism, stated that "every aspect of learning... can in principle be so precisely described that a machine can be made to simulate it." They budgeted two months for the problem. It has now taken nearly seven decades and is nowhere near "solved."
A rough timeline:
1943 — Warren McCulloch and Walter Pitts publish the first mathematical model of an artificial neuron.
1958 — Frank Rosenblatt builds the Perceptron, the first trainable neural network, generating headlines about machines that could "walk, talk, see, write."
1969–1980s — The "AI Winter." Minsky and Papert's book Perceptrons demonstrated fundamental limitations of single-layer networks, funding dried up, and the field stagnated.
1986 — Rumelhart, Hinton, and Williams popularize backpropagation, the algorithm that makes training deep networks mathematically tractable.
2012 — AlexNet wins the ImageNet competition by a landslide, proving deep convolutional networks work at scale once you have enough data and GPU power. This is widely considered the start of the modern deep learning boom.
2017 — Google researchers publish "Attention Is All You Need," introducing the Transformer architecture — the backbone of essentially every major language model since, including GPT, Claude, and Gemini.
2020s — Large Language Models (LLMs) scale into the hundreds of billions of parameters, RAG and agentic tool-use emerge, and AI shifts from a research curiosity to consumer infrastructure.
The pattern worth noticing: AI didn't improve because someone had a flash of genius about "how thinking works." It improved because of better math (transformers), more data, and more compute — three unglamorous ingredients that happened to compound.
2. Neural Networks: The Engine Room
2.1 The Biological Metaphor (and Why It's Overstated)
Neural networks are loosely inspired by biological neurons, but the resemblance is skin-deep. A real neuron is a chemically complex, self-repairing cell embedded in a living system. An artificial "neuron" is a single number produced by multiplying inputs by weights and squashing the result through a function. Calling it a "brain" is a bit like calling a wristwatch a "sundial with ambition." Useful shorthand, misleading if taken literally.
2.2 The Artificial Neuron
Each artificial neuron does exactly one job: take several numbers in, produce one number out. The math:
Where:
x1...xn are the inputs (could be pixel values, word embeddings, anything numeric)w1...wn are weights — learned values that determine how much each input mattersb is the bias — an offset that shifts the outputactivation() is a non-linear function applied to zWithout that final non-linear activation function, you could stack a thousand layers and the whole network would mathematically collapse into a single linear equation — utterly incapable of learning anything more complex than a straight line. The most common activation functions:
ReLU (Rectified Linear Unit):
f(z) = max(0, z) — simple, fast, the current default for most hidden layers.Sigmoid:
f(z) = 1 / (1 + e^-z) — squashes output between 0 and 1, useful for probabilities.Softmax: converts a vector of numbers into a probability distribution that sums to 1 — this is what sits at the very end of a language model, turning raw scores into "the probability the next word is cat."
2.3 Forward Propagation — the Math
"Forward propagation" is just the process of pushing data through the network, layer by layer, until you get an output. For a single layer, it's expressed in matrix form:
Here
W is a matrix of all the weights in that layer, X is the input vector, and B is a vector of biases. This is why AI is fundamentally linear algebra at industrial scale — a modern LLM performs trillions of these matrix multiplications per response, which is precisely why they need specialized chips (GPUs and TPUs) rather than ordinary processors.2.4 Backpropagation and Gradient Descent
Training is the process of adjusting every weight and bias so the network's output gets closer to the correct answer. This happens through:
Loss calculation — comparing the network's prediction to the true answer using a loss function, commonly cross-entropy loss for classification tasks:
Backpropagation — using calculus (specifically the chain rule) to compute how much each individual weight contributed to the error:
∂L/∂w for every weight w in the network.Gradient descent — nudging each weight slightly in the direction that reduces the error:
Repeat this process across billions of examples, millions of times, and you get a network that has, purely through trial and error, arranged its weights into a configuration that produces useful outputs. Nobody manually designs what any individual weight should be — it's discovered, not written. This is also why AI is often called a black box: even the people who build these systems cannot point to a specific weight and say "this is where it knows Paris is the capital of France." The knowledge is smeared, statistically, across billions of parameters.
2.5 Anatomy of an ANN: Six Layers, Six Jobs
A typical deep Artificial Neural Network (ANN) used for a moderately complex task can be broken into roughly six functional layers:
Input Layer — receives raw data converted into numbers (pixel intensities, word tokens, audio waveforms). No computation happens here; it's just the data's entry point.
Embedding / Encoding Layer — converts discrete inputs (like words) into dense numeric vectors that capture meaning. This is where "king" and "queen" end up mathematically close to each other.
Hidden Layer 1 (Feature Detection) — detects low-level patterns. In an image network, this might be edges or color gradients. In text, simple syntactic patterns.
Hidden Layer 2 (Feature Combination) — combines low-level features into more abstract concepts — shapes from edges, phrases from words.
Hidden Layer 3+ (Abstraction / Attention) — in transformer-based models, this is where self-attention mechanisms live, weighing how much every part of the input should influence every other part. This is the layer doing the heaviest conceptual lifting.
Output Layer — converts the final internal representation into the desired output format: a probability distribution over the next word, a classification label, or a set of pixel values.
Note: real production models like Claude or GPT don't have "six layers" in total — they have dozens to hundreds of stacked transformer blocks, each internally containing several sub-layers (attention, normalization, feed-forward). The six-layer breakdown above is a conceptual anatomy, not a literal layer count.
3. How AI "Thinks": Vectors, Embeddings, and Latent Space
This is the part that makes the "brain" metaphor collapse entirely. AI doesn't "think" the way you do — it converts everything into vectors (lists of numbers) and manipulates them geometrically.
A word becomes a vector — for example, a 4096-dimensional list of numbers.
A sentence becomes a sequence of vectors, further combined by attention into a single contextual representation.
An image becomes a grid of vectors, one per patch.
Meaning becomes distance and direction in this high-dimensional space, called latent space.
The famous demonstration of this: in a well-trained word-embedding space, the vector arithmetic
actually works, approximately. That's not poetry — that's literal subtraction and addition of number lists that happens to align with human semantic intuition. "Thinking," in an AI system, is the process of moving a point through this abstract mathematical space and reading off where it lands.
4. Reasoning in AI: Chain-of-Thought and Beyond
Early language models answered questions in a single forward pass — essentially a very sophisticated autocomplete. Modern "reasoning" models improve on this using a technique broadly called Chain-of-Thought (CoT) prompting, formalized in a 2022 paper by Wei et al. at Google. The core insight: language models produce better answers when forced to generate intermediate reasoning steps rather than jumping straight to a conclusion.
This has since evolved into more structured approaches:
Chain-of-Thought: the model writes out step-by-step reasoning in natural language before the final answer.
Tree-of-Thought: the model explores multiple reasoning branches in parallel and evaluates which path seems most promising, similar to how a chess engine considers multiple move sequences.
Self-consistency decoding: the model generates several independent reasoning chains for the same question and takes the most common final answer, on the theory that errors are inconsistent but correct reasoning tends to converge.
Extended "thinking" tokens: modern reasoning models (such as OpenAI's o-series or Claude's extended thinking mode) are trained via reinforcement learning to generate long internal reasoning traces before committing to a final answer, effectively giving the model "scratch paper."
It is worth being precise here: this is not reasoning in the human, conscious sense. It is a learned statistical tendency — generating tokens that resemble reasoning steps improves the probability of the next tokens being correct, because reasoning-like text in the training data was correlated with correct answers. The model isn't "checking its work" the way a person does; it's exploiting a statistical regularity that happens to resemble checking your work.
5. Retrieval-Augmented Generation (RAG) — Full Breakdown
5.1 Why RAG Exists
A language model's knowledge is frozen at the moment its training finished — this is called the knowledge cutoff. Ask it about something that happened afterward, and it will either admit ignorance or, worse, hallucinate — confidently invent a plausible-sounding but false answer. RAG was introduced specifically to solve this, in a 2020 paper by Lewis et al. at Facebook AI Research, titled "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."
The core idea is deceptively simple: instead of relying purely on what the model memorized during training, give it access to an external, searchable knowledge base at the moment it answers a question.
5.2 The RAG Pipeline Step by Step
A production RAG system typically works like this:
Document Ingestion Source documents (PDFs, web pages, internal wikis, support tickets) are collected and cleaned.
Chunking Documents are split into smaller passages — usually 200 to 1,000 tokens each — because embedding an entire book as one vector would blur its meaning into uselessness. Chunking strategy (fixed-size, sentence-based, or semantic chunking) has a major effect on retrieval quality.
Embedding Each chunk is passed through an embedding model (such as OpenAI's
text-embedding-3 or Anthropic's Voyage embeddings) that converts it into a dense vector — typically 384 to 3072 dimensions — representing its meaning numerically.Storage in a Vector Database These vectors are stored in a specialized database optimized for similarity search (more on this below).
Query Embedding When a user asks a question, that question is also converted into a vector using the same embedding model.
Similarity Search / Retrieval The database compares the query vector against all stored document vectors, typically using cosine similarity:
It returns the top-k (often 3 to 10) most similar chunks — the passages "most relevant" to the question mathematically.
Re-ranking (optional but common) A secondary, more computationally expensive model re-scores the retrieved chunks for relevance, since raw vector similarity is fast but imprecise.
Augmentation The retrieved chunks are inserted into the language model's prompt, alongside the user's original question — something like: "Using the following context, answer the question: [retrieved chunks] [user question]."
Generation The language model generates its final answer, now grounded in the retrieved, current, factual material rather than solely its frozen training memory.
5.3 Vector Databases
A vector database is purpose-built for one job: finding "nearest neighbors" in high-dimensional space, fast, at scale. Traditional databases (SQL, for instance) are built to find exact matches or ranges — they are terrible at answering "which of these ten million items is conceptually closest to this one?" Vector databases solve this using Approximate Nearest Neighbor (ANN) search algorithms — most commonly HNSW (Hierarchical Navigable Small World) graphs, which trade a small amount of accuracy for enormous speed gains.
Common vector database products include Pinecone, Weaviate, Milvus, Qdrant, Chroma, and vector extensions bolted onto existing databases like pgvector for PostgreSQL.
5.4 Pros, Cons, Uses, and Benefits
Benefits:
Keeps answers current without retraining the entire model, which can cost millions of dollars.
Reduces hallucination by grounding answers in real, retrievable source material.
Allows citation — the system can point to which document supported a claim.
Enables private, proprietary knowledge (internal company documents) to be queried without ever putting that data into the model's actual weights.
Drawbacks:
Retrieval quality is only as good as the chunking and embedding strategy — bad chunking produces irrelevant context, which produces bad answers.
Adds latency — an extra search step before generation begins.
Struggles with questions requiring synthesis across many documents rather than a single relevant passage.
Vector similarity is not the same as truth — a document can be semantically similar to a question while being factually wrong or outdated, and the system has no inherent way to know that.
Common uses: customer support chatbots grounded in a company's own documentation, legal and medical research assistants, enterprise "chat with your documents" tools, and coding assistants that retrieve relevant snippets from a large codebase before answering.
6. Benchmarks: How We Grade a Machine's Mind
Since there's no universal IQ test for software, the field relies on standardized benchmarks — curated test sets designed to measure specific capabilities. A few of the most cited:
MMLU (Massive Multitask Language Understanding): roughly 16,000 multiple-choice questions across 57 subjects, from law to astronomy, used as a general knowledge and reasoning benchmark.
HumanEval: measures a model's ability to write correct, functioning code from natural-language problem descriptions.
GPQA (Graduate-Level Google-Proof Q&A): extremely difficult science questions designed so that even a human expert with internet access struggles to answer quickly — meant to test genuine reasoning rather than lookup ability.
SWE-bench: evaluates whether a model can resolve real, historical GitHub issues in real codebases — a much closer proxy for "can this thing actually do a software engineer's job" than toy coding puzzles.
ARC-AGI: a benchmark specifically designed to resist memorization, testing abstract pattern reasoning on novel puzzle types the model has never seen.
A quiet but important criticism worth noting: benchmarks are frequently gamed, intentionally or not, through data contamination — when benchmark questions leak into a model's training data, inflating scores without reflecting real capability. This is why the field has increasingly moved toward "held-out," frequently refreshed, or dynamically generated benchmarks — an arms race between test-makers trying to measure genuine ability and models that are, whether by accident or incentive, very good at memorizing the answer key.
7. How AI Generates Images
Modern image generation (Midjourney, DALL·E, Stable Diffusion, Imagen) is built almost entirely on diffusion models. The workflow:
Text Encoding — the prompt is converted into embeddings using a text encoder (commonly a CLIP-style model), which understands the relationship between words and visual concepts.
Noise Initialization — the process starts with pure random noise, a grid of meaningless static, the visual equivalent of TV snow.
Iterative Denoising — a neural network, trained to predict "what noise was added to this image," repeatedly subtracts a small amount of predicted noise from the canvas, guided by the text embedding at every step. This happens across dozens to hundreds of steps.
Classifier-Free Guidance — at each denoising step, the model compares what the image would look like with the text prompt's influence versus without it, and exaggerates the difference to keep the output faithfully aligned to the prompt.
Decoding — many modern systems (like Stable Diffusion) work in a compressed "latent" space rather than full pixel space for efficiency, so a final decoder network expands the small latent grid back into a full-resolution image.
The training process that makes this possible works in reverse: the model is shown millions of real images with deliberately added noise at various intensities, and trained to predict and remove that noise. Do this enough times across enough images, and the network essentially learns "what does a plausible image look like," which it can then apply to pure random noise, sculpting chaos into a coherent picture, guided the whole way by your text prompt.
8. How AI Generates Video
Video generation (Sora, Veo, Runway) is the same diffusion principle, made dramatically harder by adding a time axis. The pipeline:
Prompt and Reference Encoding — text (and sometimes a reference image or video) is embedded, same as image generation.
Spatiotemporal Latent Representation — instead of denoising a single 2D grid, the model works on a 3D block of latent "patches" spanning both space and time, so it can reason about motion, not just appearance.
Joint Denoising Across Frames — the diffusion process runs across the entire clip simultaneously (not frame-by-frame independently), which is what prevents flickering, morphing objects, and the uncanny inconsistency that plagued early video models.
Temporal Attention Layers — special attention mechanisms allow the model to track "this object in frame 1 should still be this object, in a physically plausible new position, in frame 40" — effectively learning intuitive physics from watching enormous quantities of real video.
Decoding and Upscaling — the final latent block is decoded into full-resolution frames, often followed by a separate upscaling and interpolation pass to boost resolution and frame rate.
The reason video generation trails image generation in quality and cost efficiency is straightforward: an image is one frame; a five-second clip at 24 frames per second is 120 correlated frames that must remain internally consistent, obey rough physics, and follow a prompt — an exponentially harder optimization problem.
9. How AI Searches Online
When a chatbot appears to "search the internet," it is not maintaining a live crawl of the web itself. The pipeline typically looks like this:
Query Formulation — the model reformulates your natural-language question into one or more concise search queries, the way a human would type into a search bar rather than paste in a full paragraph.
Search API Call — those queries are sent to an actual search engine's index (Google, Bing, or a specialized search provider), which returns a ranked list of URLs and snippets.
Result Selection — the model evaluates which returned pages are actually relevant, filtering out spam, ads, and low-quality sources.
Fetching and Extraction — for pages worth reading in full, the system fetches the raw page and extracts the readable text, stripping navigation menus, ads, and boilerplate.
Synthesis — the extracted content is fed into the model's context window alongside your original question, essentially functioning as a live, on-demand version of the RAG pipeline described earlier, except the "database" is the entire indexed web rather than a fixed private document set.
Citation — the model attributes specific claims back to specific sources, so a reader can verify the underlying information rather than trusting the model's word alone.
This is, functionally, RAG applied to the open web instead of a curated document set — which is why understanding RAG's mechanics earlier in this article is not a tangent; it's the exact same architecture doing a different job.
10. How AI Does Coding
Code generation models are trained on the same transformer architecture as general language models, just with a training diet heavily weighted toward source code repositories, documentation, and — critically — the relationships between code and its outcomes (does it compile, does it pass tests). A modern coding agent workflow looks like:
Context Gathering — the model reads relevant files, directory structure, and documentation to understand the existing codebase rather than working in isolation.
Planning — for non-trivial tasks, the model often generates an explicit plan or breaks the task into subtasks before writing any code (a coding-specific application of Chain-of-Thought).
Code Generation — the model predicts code token-by-token, the same next-token prediction mechanism used for prose, just trained on syntax-heavy data.
Tool Use / Execution — advanced coding agents don't just write code; they can run it, read the terminal output or error messages, and iterate — a feedback loop much closer to how a human developer actually debugs.
Testing and Verification — the model may write or run tests to confirm the code behaves as intended, rather than trusting its own first draft.
A short illustrative example of what "next-token prediction applied to code" actually looks like under the hood:
The model isn't reasoning about mathematical induction. It has seen this shape of function thousands of times in training data and learned, statistically, what token is most likely to come next given everything before it — and it turns out that process, applied at sufficient scale, produces genuinely useful code.
11. Training Data — And the Uncomfortable Question of Who Pays For What
Here is where the conversation gets less about elegant mathematics and more about business models. Large language models require staggering quantities of training data — text scraped from books, websites, code repositories, and, increasingly, user conversations with the AI systems themselves.
This creates a genuinely uncomfortable situation for paying customers. As multiple industry reports have detailed, Anthropic changed its consumer data policy around late September 2025: conversations from Free, Pro, and Max subscribers — people paying real money for the service — became eligible for use in training future models by default, unless the user manually opts out in privacy settings. Retention for training-eligible data extends up to five years, a significant jump from the prior 30-day deletion window. Business, Enterprise, and API-based accounts are generally contractually excluded from this and are not used for training. Consumer subscribers, however, must actively find and disable the "Help improve Claude" toggle if they want their conversations excluded.
There is a real irony worth sitting with here: subscribing to a product typically buys you a service free of being the product. With consumer AI subscriptions, that assumption doesn't automatically hold — a paying user's conversations can, unless they opt out, still become raw material for the next model iteration, meaning the same person is simultaneously the customer and the unpaid data contributor. It is not fraud, and it is disclosed in the terms of service, but "disclosed in the terms of service" has never once been synonymous with "widely understood by the people it affects." If you want to know whether your own conversations are being used, the honest answer is: check your account's privacy settings directly, because policies and defaults change, and the specifics matter more than any summary.
This is a broader pattern in the industry, not unique to one company. It reflects a genuine tension: models need continuously fresh, high-quality conversational data to keep improving, and real user conversations are extraordinarily valuable for that purpose in a way that scraped web text is not — precisely because they represent authentic, effective human-AI interaction. The fix is not to feel powerless about it, but to actually go and check the toggle.
12. The Claude Code Leak of March 31, 2026
On March 31, 2026, between roughly 00:21 and 03:29 UTC, Anthropic accidentally exposed the complete internal source code of Claude Code, its terminal-based agentic coding tool, to the public internet. Here is what actually happened, mechanically:
Anthropic's build process for Claude Code uses Bun as its bundler. Bun generates JavaScript source map (.map) files by default during builds — debugging artifacts that map minified, production code back to its original, fully readable source.
When version 2.1.88 of the
@anthropic-ai/claude-code npm package was published, a 59.8 MB source map file was accidentally included in the public package rather than being excluded via .npmignore.That source map contained a reference to an archive hosted on Anthropic's cloud storage, effectively pointing straight at the full, human-readable original codebase.
Within hours of publication, an intern at Solayer Labs discovered the exposure and posted about it publicly, and the codebase — roughly 512,000 lines of TypeScript across approximately 1,900 files — was mirrored across GitHub and analyzed extensively by the developer community before Anthropic could fully contain it.
What was found inside it:
The full agent harness architecture — the orchestration layer that wraps the underlying Claude model and gives it the ability to use tools, run shell commands, manage files, and coordinate multiple sub-agents on a task.
A three-layer memory architecture and context-compaction strategies used to manage long coding sessions without exceeding context limits.
Roughly 44 hidden, unreleased feature flags, revealing product features Anthropic was building but had not shipped publicly — including an autonomous background-operation mode (internally codenamed KAIROS) intended to let the agent work persistently without a user actively present, along with related unreleased modes for offline/"away" operation.
Internal permission-sandbox logic governing what actions the coding agent is and isn't allowed to take autonomously.
Assorted internal codenames, developer comments, and even a hidden novelty feature (an embedded virtual pet, described by multiple reports as a "Tamagotchi" easter egg), which — in the way these things go — got almost as much attention online as the serious architectural revelations.
Why it mattered strategically, not just technically: competitors building their own AI coding agents (Cursor, GitHub Copilot, Windsurf, OpenAI's Codex, among others) suddenly had a detailed, real-world blueprint of how a production-grade agentic harness is actually engineered — something previously guessed at from the outside. Multiple industry analyses concluded that the leak reinforced an argument already circulating in the field: that the "harness" wrapping an AI model is not, by itself, a durable competitive moat, since it can be reverse-engineered or replicated once exposed — meaningful differentiation increasingly has to come from the underlying model's raw capability rather than the tooling around it.
Separately, but confusingly overlapping in time: during that exact same window, an unrelated supply-chain attack hit the popular
axios npm package, publishing malicious versions containing a Remote Access Trojan (RAT). Anyone who happened to run npm install or update Claude Code during that specific 00:21–03:29 UTC window was advised to check their lockfiles for the compromised versions and treat any affected machine as potentially compromised. This was coincidental timing with a genuinely separate incident, not caused by the Claude Code leak itself, but the overlap made the initial hours of the story considerably more chaotic and difficult to disentangle for the developers trying to figure out what, exactly, had just happened to their machines.Anthropic subsequently pursued DMCA takedowns against repositories hosting the leaked source and shifted its recommended installation method toward a standalone native installer, reducing reliance on the npm dependency chain that made the incident possible in the first place. Claude Code itself remains closed-source, proprietary software; the leak did not change its official licensing or availability — it just meant, for a few chaotic hours, that the entire internet got an uninvited look at the blueprint.
Conclusion: There Is No Ghost, Just Very Good Bookkeeping
Strip away the branding, the anthropomorphic language, and the increasingly cinematic marketing videos, and what remains is this: matrices multiplying matrices, gradients nudging weights, vectors clustering by meaning, and enormous pipelines of retrieval, denoising, and prediction stacked on top of each other with genuine engineering sophistication. None of it requires belief in machine consciousness to be useful, and none of it requires cynicism about its usefulness to stay clear-eyed about how it actually works — and who actually benefits from your data along the way.
The "brain" was never a brain. It was always vectors and math, arranged remarkably well.
References
McCulloch, W. & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity.
Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning Representations by Back-Propagating Errors. Nature.
Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks (AlexNet).
Vaswani, A. et al. (2017). Attention Is All You Need. Google Research / NeurIPS.
Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Facebook AI Research.
Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Google Research.
VentureBeat (March 31, 2026). Claude Code's source code appears to have leaked: here's what we know.
Zscaler Security Research (2026). Anthropic Claude Code Leak — Critical AI Security Threat 2026.
DEV Community (April 1, 2026). The Great Claude Code Leak of 2026.
Tom's Guide (2026). Your Claude chats are being used to train AI — here's how to opt out.
MPG ONE (2026). Does Anthropic Train Claude on Your Data? Full Answer.
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Posted Aug 25, 2026
See what powers modern AI—from embeddings and attention to training, RAG, reasoning, image generation, web search, and coding—without the marketing fog.
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Jul 16, 2026 - Jul 17, 2026
