What is Claude? (Anthropic’s AI Assistant)

Claude is a family of large language models (LLMs) built by Anthropic, an AI safety and research company founded in 2021 by former OpenAI leads (and siblings) Dario and Daniela Amodei.

The company’s name is a nod to Claude Elwood Shannon, the father of information theory. The mathematician whose work laid the foundation for digital communication, computing, and, by extension, modern AI.

A fitting tribute.

Claude’s Origins and Purpose

The company claims that its identity is based on its ethical framework, often summarized by Anthropic as “Helpful, Honest, Harmless” (sometimes abbreviated HHH). The idea is to build AI systems that are, sure, powerful, but also safe, reliable, and aligned with human values.

For example, through Constitutional AI, Claude is trained with a “constitution,” a set of rules or principles that inform what kinds of outputs are acceptable or not.

At least, that’s what their “values” page says. As you’ll see in the next section, despite their claims, human values may not be at the top of their priority list.

Okay, so what about the company’s purpose? Claude is said to have been built in response to several major gaps in conversational AI up to now. Some of these are:

• Safety risks: Many AI models generate content that is biased, harmful, or misleading. Claude aims to reduce these through better alignment and value-grounded training.
• Trust and transparency issues: Users often want to know why a model said what it said, or when it doesn’t know something. Claude’s design (including admitting uncertainty or following ethical rules) is trying to build that trust.
• Lack of clarity and coherence in long tasks: Earlier models sometimes struggle when a conversation or task becomes long and complex. Claude (especially newer model versions) pushes toward larger context windows, reasoning over many steps, better tool use, and memory.

Anthropic’s Legal Hot Water

Despite Anthropic’s claims, there’s a little controversy to unfold here.

You see, the company isn’t without its fair share of, hmm, legal challenges. Uh-huh, Anthropic has found itself at the center of several lawsuits over how Claude was trained.

A major class action brought by authors including Andrea Bartz, Charles Graeber, and Kirk Wallace Johnson alleged that the company used millions of books in training Claude, some of which were sourced from so-called “shadow libraries” without proper licenses.

In June 2025, Judge William Alsup of the U.S. District Court ruled that training on legitimately acquired books qualified as fair use, but that maintaining pirated copies in a “central library” did not.

To avoid taking that issue to trial, Anthropic agreed in September 2025 to a proposed US$1.5 billion settlement. The deal works out to about US$3,000 per book across roughly 500,000 titles, requires the company to destroy copies of the pirated books, and comes without any admission of liability.

The company is also facing legal action from major music publishers.

Universal Music Group, Concord Music Group, and ABKCO sued Anthropic in late 2023, alleging that Claude had been trained on copyrighted lyrics from more than 500 songs and that it sometimes reproduced those lyrics in responses. Works by Beyoncé and Maroon 5 were among those cited in the complaint.

In early 2025, a judge denied the publishers’ request for an injunction that would have forced Anthropic to halt such outputs and dismissed some claims, though the core allegations of copyright infringement remain.

A partial settlement followed, under which Anthropic committed to strengthening guardrails preventing Claude from reproducing lyrics too closely and to restricting the model’s handling of song lyrics in certain contexts. Even so, the litigation is still ongoing.

For Anthropic, the outcomes so far have been expensive and reputationally risky, and they also point to a wider industry problem: until licensing standards and dataset sourcing are clarified, legal and ethical uncertainty will remain baked into the field.

How Claude Works: Under the Hood

Controversies aside, let’s take a look at what makes Claude’s models tick:

Training Data and Datasets

Claude Opus 4 and Sonnet 4 were trained using a proprietary mixture of data sources. According to Anthropic, that includes publicly available information from the internet, non-public third-party data, data from annotation services and paid contractors, data generated internally, and user data, but only from users who have opted in (which, as I just covered in the last section, we know isn’t the full story).

The training pipeline includes data cleaning steps like deduplication and classification. They also use a general-purpose web crawler for public pages (respecting robots.txt, avoiding password-protected or CAPTCHA pages), and apply diligence checks to filter out low-quality or problematic inputs.

What is less clear or still evolving:

• The exact breakdown by source: how much user-opted data vs. third-party licensed vs. public web content.
• How balanced the datasets are in terms of domain (e.g., code vs text vs images), language diversity, or representation of underserved or niche topics.
• The proportions of raw/unlabeled vs annotated data, and how much human labeling was involved.

Training Processes and Methods

Anthropic uses multiple phases in training Claude:

• Pretraining on large corpora: large, diverse datasets (public + private + internal + some user-opted data) to build foundational language, reasoning, and knowledge abilities.
• Fine-tuning phases: These include methods like RLHF (Reinforcement Learning from Human Feedback) and the Constitutional AI approach. Constitutional AI uses a set of guiding ethical principles (a “constitution”) to help the model self-critique, revise outputs, and prioritize safer/honest/harm-averse responses.
• Character training/trait shaping: Claude is shaped to develop traits like open-mindedness, curiosity, and nuance. For instance, part of the model’s behavior is governed by rules about what kinds of responses are “too preachy,” “condescending,” or excessive so that it avoids those styles.

Safety, Bias, and Alignment

To reduce harmful outputs and align the model with Anthropic’s stated values, Claude incorporates multiple mechanisms:

• Use of principles through Constitutional AI (including ethical norms, e.g., avoiding discriminatory or toxic content; avoiding assisting with illegal/unethical requests).
• Filters and safety evaluations: Safety testing is part of the model release. In the system card for Claude 4, Opus 4 is classified under a higher safety standard (ASL-3) than Sonnet 4 (ASL-2).
• Bias remains a concern: there are likely biases stemming from data sources (representation, domain imbalance, language, etc.), though publicly released model cards do not show all internal bias audit results. Some output issues have been reported in generative AI more broadly (not always Claude-specific), especially around underrepresented languages or cultural contexts.

Updates and Recent Policy Changes (especially around training using user data)

A recent shift: as of August 28-29, 2025, Anthropic announced that its consumer-tier Claude users (Free, Pro, Max, and Claude Code) must decide whether to allow their chat transcripts and coding sessions to be used for model training.

If users opt in or do not act by the deadline (September 28, 2025), their data will be retained for up to five years and used in training. Previously, consumer data was deleted after 30 days (unless legal/policy exceptions applied).

The update does not affect business, government, education, or API users; those categories are exempt.

This change has implications for privacy, trust, and performance:

• Users concerned about private or sensitive conversations must opt out proactively.
• Longer retention may allow better detection of abuse, misuse, or safety risks over time (e.g., spotting recurring patterns), which could improve performance and safety classifiers. Anthropic argues this is a benefit.
• But it raises questions around data ownership, transparency (which data will be used and how it’s filtered), and the risk that opt-in defaults may lead to many users being included without full awareness.

What Can Claude Do? Use Cases and Capabilities

Alright, to the important stuff: What Claude models actually do? Let’s take a look:

Major Use Cases

Claude’s architecture supports a broad mix of applications, many of which lean into its strengths in alignment, reasoning, and safety controls.

One of its clearest domains is content creation: writing, summarization, drafting emails, blog posts, social media content, ad copy, or reports. Because Claude can absorb large context windows and preserve stylistic consistency, it’s frequently used to turn bullet points into long-form narratives, rewrite texts, or generate variations.

But Claude goes well beyond prose. Anthropic itself has published that the top use cases include web and mobile application development, academic and business research, and strategic analysis. In Anthropic’s internal “Clio” system (which tracks how clients use Claude), tasks like optimizing business strategy or augmenting research workflows are common.

Claude also tackles coding, code generation, and reasoning tasks. For instance, it has demonstrated strong performance on SWE-Bench, a benchmark assessing real-world software engineering tasks: in one version, Claude 3.5 Sonnet scored ~49% (surpassing prior models).

Finally, as the models mature, Claude becomes more proficient at multimodal capabilities (handling text + images). While specific performance metrics are less public, newer versions of Claude are increasingly marketed as “tool-aware” and able to use external APIs or interpret structured input (e.g., image descriptions, document uploads) in workflow integrations.

Performance and Strengths

Where Claude shines is in blending safety, alignment, and nuance. Because of its training with Constitutional AI and internal safety mechanisms, Claude is often more conservative in ambiguous or sensitive contexts, choosing to refuse or hedge rather than outright mislead. That tends to earn trust in settings (like business or regulated domains) where hallucinations are costly.

In benchmarks, Claude 4 models regularly outperform earlier versions on reasoning and code tasks. For example, in head-to-head evaluations with competitors, Claude 4’s reasoning and agentic behavior are often cited as superior, though detailed benchmark scores vary by task.

Another strength is context awareness. Claude’s models now support context windows up to ~200,000 tokens (depending on the variant), meaning they can reason over very long documents without losing track.

Image source.

Because of its focus on safe behavior, Claude’s outputs are typically more cautious; it is less likely to make bold, unverified claims or become inconsistent mid-conversation (though that doesn’t mean consistency is perfect).

Limitations and Weak Spots

Claude has its trade-offs. One of the biggest is, of course, hallucination risk. Even with safety tuning, Claude can still make up facts, especially in niche domains or queries where the training data is thin. Because it errs on the side of caution, it may understate or refuse to answer some valid questions.

Another limit is domain specificity: in highly technical or specialized fields (e.g., advanced medical, cryptography, law), Claude may lack the depth or currency of expert systems or domain-fine-tuned models.

Even with large context windows, extremely long reasoning chains can degrade quality: subtle thread consistency, memory retention, and logic across many steps still challenge all LLMs, Claude included.

There is also a trade-off between depth and latency / compute cost. More powerful variants (e.g., Opus) deliver better reasoning but demand more computational resources and may respond more slowly or cost more. Some users may prefer “lighter” variants for simpler tasks.

Bias and imperfect alignment remain open issues: Claude’s safety rules are not perfect, and some bias from training data can still seep through (especially around underrepresented languages, cultures, or fringe viewpoints).

In extreme “agentic” or tool-enabled settings, misuse or adversarial prompting remains a concern. (Recent research on evaluating harmful behavior in computer-using agents suggests that even strong models can be coaxed into harmful acts under certain settings.)

Comparison with Other AI Assistants

Before I sign off, the important part: how do Claude models stack up against the competition? Let’s find out:

Claude vs ChatGPT / OpenAI Models

LLM4DS evaluated Claude 3.5 Sonnet, ChatGPT, Copilot, and others on data science code tasks; while all models exceeded a 50 % success baseline, Claude and ChatGPT were the only ones consistently above 60 %, with Claude’s performance showing more variance as task difficulty increased.

In a more narrowly focused calculus benchmark, researchers tested ChatGPT 4o, Claude Pro, and others across 13 differentiation and optimization problems. ChatGPT led (94.71 % success) while Claude Pro came in second at 85.74 %.

On the community / crowd side, testers have run small prompt sets to compare Claude and newer models: for instance, a GitHub “200 prompt” challenge pitted GPT-5 vs Claude 4 Sonnet, tracking factual precision, latency, and hallucination rate in mixed tasks.

Meanwhile, Tom’s Guide tested Claude, Gemini, and Grok across seven real-world prompts, finding Claude excelled in ethical reasoning and writing style, though not always in speed or fresh knowledge.

These studies show that Claude often competes strongly, especially in reasoning and reduction of hallucinations, though performance is not uniformly dominant across all task types or domains.

Conclusion and Next Steps

Here’s your TL;DR:

• Claude is built around Anthropic’s “helpful, honest, harmless” philosophy and Constitutional AI, setting it apart from rivals that rely more on human feedback.
• It shines in long-context reasoning and cautious outputs but still hallucinates, faces lawsuits over training data, and can be overly hesitant.
• Benchmarks show Claude competitive with ChatGPT and Gemini in some areas, weaker in others, proving no AI assistant is flawless.

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