How Does Anthropic Claude Work?

Anthropic Claude is a family of large language models (LLMs) developed by Anthropic, an AI safety company founded in 2021 by former OpenAI researchers including Dario and Daniela Amodei. Claude stands apart in the AI landscape due to its foundational emphasis on safety, honesty, and helpfulness — principles encoded directly into its training pipeline through innovative techniques like Constitutional AI and RLHF.

At its core, Claude is an autoregressive language model built on the Transformer architecture. It processes input tokens and generates output by predicting the most likely next token, one step at a time. However, what truly distinguishes Claude is the multi-phase training pipeline that shapes its behavior — from raw pattern recognition to nuanced, value-aligned reasoning.

Footnotes

1. Anthropic — About Us — Anthropic's mission and founding principles around AI safety. ↩

The Transformer Foundation

Like other modern LLMs, Claude is built on the decoder-only Transformer architecture first introduced by Vaswani et al. in 2017. The key components include:

The self-attention mechanism computes a weighted sum over all previous tokens for each position, governed by the equation:

$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$

where $Q$, $K$, and $V$ are query, key, and value projections of the input. Claude models use Grouped-Query Attention, which strikes a balance between multi-head attention quality and multi-query attention efficiency.

Footnotes

1. Anthropic — Model Card for Claude 3 — Technical details on Claude 3 architecture, including GQA and attention mechanisms. ↩

Constitutional AI in Depth

Constitutional AI is arguably the most distinctive aspect of Claude's development. The constitution consists of approximately 16 principles organized into categories:

The CAI process has two main phases:

1. Supervised Learning Phase (SL-CAI): The model generates a response to a prompt, critiques it using a constitutional principle, and then revises it. The revised (prompt, response) pairs are used to fine-tune the model via supervised learning.

2. Reinforcement Learning Phase (RL-CAI): The fine-tuned model generates two responses to a prompt. A special "critique" model evaluates both against the constitution and selects the preferred one. These preferences train a reward model, which then guides RL optimization — analogous to RLHF, but with AI-generated preferences replacing human comparisons.

This AI-generated feedback approach is critical because it allows Anthropic to scale alignment training far beyond what purely human-labeled data could provide.

Footnotes

1. Anthropic — Constitutional AI: Harmlessness from AI Feedback — The foundational paper on Constitutional AI methodology and constitutional principles. ↩

2. Anthropic — RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback — Detailed technical paper on using AI-generated feedback as a scalable substitute for human preferences. ↩

The Claude 3 and 3.5 Model Family

Anthropic released the Claude 3 family in March 2024, introducing a tiered model architecture with three tiers:

The Claude 3.5 generation (released mid-2024) introduced further improvements, including enhanced coding ability in Sonnet 3.5, tool use, and computer use capabilities that allow Claude to interact with graphical user interfaces.

Footnotes

1. Anthropic — Claude 3 Model Family — Announcement and specifications for Haiku, Sonnet, and Opus tiers. ↩

2. Anthropic — Claude 3.5 Sonnet and Computer Use — Release details for Claude 3.5 Sonnet including tool use and computer interaction capabilities. ↩

Inference: How Claude Generates Text

When you send a message to Claude, the following process occurs at inference time:

1. Tokenization: Your input is converted into a sequence of token IDs using a BPE tokenizer.

2. Embedding: Each token ID is mapped to a dense vector (embedding), then combined with positional information via RoPE.

3. Transformer Forward Pass: The token embeddings pass through $L$ transformer layers, each performing multi-head self-attention (with GQA) and feed-forward computations. At each layer, the representation of every token is updated based on information from all other tokens in the context.

4. Logit Prediction: The final hidden state of the last token position is projected through a linear layer to produce a logit vector over the entire vocabulary.

5. Sampling: The logits are converted to probabilities via softmax: $P(x_t) = \frac{e^{z_t / T}}{\sum_{j} e^{z_j / T}}$ where $T$ is the temperature parameter. The next token is sampled from this distribution.

6. Autoregressive Loop: Steps 3–5 repeat, with the newly generated token appended to the context, until a stop condition is met (end-of-sequence token, maximum length reached, or user-defined stop sequence).

Safety Architecture: Defense in Depth

Claude's safety is not a single mechanism but a layered system — a defense-in-depth approach that operates across training and inference:

This multi-layered approach means that even if an adversarial prompt bypasses one safety mechanism, the model's deeply ingrained Constitutional AI training, combined with output monitors, provides additional barriers. The philosophical design principle is that no single layer should be trusted as the sole safety mechanism.

Footnotes

1. Anthropic — Responsible Scaling Policy — Framework for evaluating and mitigating risks of increasingly capable AI systems. ↩