Anthropic Write for Us: Claude Guest Posts and Guidelines

Anthropic develops the Claude family of artificial intelligence models
and products. Developers and organizations use Claude for language,
analysis, coding, document processing, research assistance,
conversational applications, and tool-enabled workflows.

Computer Tech Reviews welcomes practical contributions about Anthropic,
Claude, API development, model evaluation, prompt design, tool use,
enterprise implementation, coding workflows, safety research, and
responsible deployment.

We invite articles from AI developers, researchers, software engineers,
product managers, security specialists, technology consultants,
educators, and people with first-hand experience testing or
implementing Anthropic products.

This contributor page belongs to our broader

Artificial Intelligence Write for Us

hub, which covers additional AI models, platforms, technologies, and
applications.

What Is Anthropic?

Anthropic is an artificial intelligence research and product company.
Its products include Claude applications, developer APIs, coding tools,
and enterprise services built around its model family.

Anthropic also publishes research and documentation related to model
behaviour, interpretability, safety evaluations, alignment, societal
impacts, economic effects, and frontier-risk governance.

Articles about Anthropic should distinguish between the company, the
Claude model family, Claude applications, Claude Code, the Anthropic
API, and access through third-party cloud platforms. These are connected
but not interchangeable products.

Anthropic Topics We Welcome

We accept original tutorials, technical explainers, implementation
guides, product evaluations, comparisons, case studies, and informed
analysis about:

• Anthropic and the Claude model family
• Building applications with the Anthropic API
• Prompt design and system instructions
• Structured responses and data extraction
• Tool use and function calling
• Retrieval-augmented generation with Claude
• Working with long documents and context
• Claude for coding and software development
• Claude Code workflows and governance
• Multimodal document and image analysis
• Streaming and application responsiveness
• Managing tokens, latency and cost
• Prompt caching and repeated context
• Testing and evaluating Claude applications
• Reducing hallucinations and unsupported answers
• Privacy and security in Anthropic integrations
• Human review and high-impact use cases
• Anthropic’s safety and interpretability research
• Claude’s Constitution and model behaviour
• Anthropic’s Responsible Scaling Policy
• Comparing Claude with other AI model families
• Common Anthropic API implementation mistakes

Claude Applications and the Anthropic API

Users can interact with Claude through Anthropic’s applications, while
developers can integrate supported Claude models into their own
software through the Anthropic API. Model access may also be available
through supported cloud platforms.

A developer-focused article should identify:

• The exact model identifier and date of testing
• The API, SDK or cloud platform used
• Relevant SDK and dependency versions
• System instructions and important prompt settings
• Input and output structure
• Token limits and response controls
• Tools, retrieval sources or integrations
• Error handling, retries and rate limits
• Privacy and data-retention requirements
• The evaluation method and expected results

Articles should not assume that a feature available in the Claude
application is automatically available through every API, model, cloud
provider, region, or account type.

Prompt Design for Claude

Effective prompting begins with a clear task, relevant context,
boundaries, and an expected output format. Longer prompts are not
necessarily better; unnecessary instructions can create conflicts or
make evaluation more difficult.

Useful prompt-engineering articles may discuss:

• Separating instructions from source material
• Defining the audience and desired output
• Providing examples for difficult formats
• Requesting citations or evidence from supplied documents
• Handling missing or uncertain information
• Preventing instructions inside documents from taking control
• Testing prompts across multiple realistic inputs
• Versioning prompts alongside application code

Prompt quality should be judged using repeatable tests rather than a
small number of carefully selected demonstrations.

Tool Use and Agentic Workflows

Claude can be integrated with tools that search data, call APIs,
perform calculations, update systems, or complete other permitted
actions. The model may decide when to request a tool, but the surrounding
application remains responsible for validating, authorizing, executing,
and recording the action.

A production tool-use workflow should consider:

• Clear tool names and descriptions
• Strict input schemas
• User and role permissions
• Validation before execution
• Confirmation for consequential actions
• Protection against prompt injection
• Timeouts and external-system failures
• Logging and audit trails
• Limits on repeated or expensive actions
• Human review for sensitive decisions

Writers should avoid describing every tool-enabled workflow as a fully
autonomous agent. Explain what the model decides, what the application
controls, and where human authorization is required.

Claude for Software Development

Claude can assist with understanding code, planning changes, generating
functions, writing tests, investigating errors, reviewing
documentation, and working across software repositories.

Generated code still requires review. It may contain security
vulnerabilities, incorrect assumptions, unsupported dependencies,
performance problems, licensing concerns, or changes that pass a narrow
test while breaking another part of the system.

Strong coding articles should identify the repository context, tools,
permissions, model, test suite, evaluation criteria, human corrections,
and safeguards used during the workflow.

Document Analysis and Retrieval

Claude applications may process documents directly or work with
retrieved information supplied by an external knowledge system. These
approaches can support summarization, question answering, comparison,
extraction, and research assistance.

Authors should explain whether the model received complete documents,
selected excerpts, search results, or information retrieved from a
vector or keyword index.

A useful evaluation should consider:

• Whether the answer is supported by the supplied material
• Whether cited passages actually support the claim
• Handling of conflicting or outdated documents
• Performance when the answer is absent
• Access permissions for retrieved information
• Resistance to malicious instructions inside documents
• Disclosure of uncertainty and missing evidence

Evaluating Claude

Model evaluation should reflect the intended application. General
benchmarks may provide context, but they do not prove that a model will
perform reliably on a company’s documents, users, tools, languages, or
workflows.

An evaluation may consider:

• Factual accuracy and evidence support
• Instruction and format adherence
• Consistency across repeated tests
• Handling of ambiguity and missing information
• Coding correctness and test results
• Tool-selection and argument accuracy
• Safety and refusal behaviour
• Prompt-injection resistance
• Latency, token use and total cost
• Performance across languages and user groups
• Amount of human correction required

Model comparisons should use the same task, source material, tools,
prompt intent, evaluation rubric, and resource constraints whenever
possible. Authors must identify the models and testing date because
model behaviour and availability change.

Anthropic, Claude and AI Safety

Anthropic publishes research and policies related to AI safety,
interpretability, alignment, model evaluation, and frontier risks. It
also publishes Claude’s Constitution, which describes principles used
in shaping model behaviour.

Anthropic’s Responsible Scaling Policy is a framework for evaluating
and managing severe risks from increasingly capable AI systems. Writers
may analyze these materials, but they should distinguish Anthropic’s
stated policies from independent evidence about how well those policies
work in practice.

Responsible implementation by a customer still requires its own access
controls, testing, privacy review, monitoring, human oversight, and
incident response. Using an Anthropic model does not transfer all
responsibility to Anthropic.

Anthropic vs. DeepSeek

Comparisons between Anthropic and DeepSeek may examine model access,
deployment choices, API design, reasoning performance, coding,
multilingual use, pricing, data handling, documentation, safety
controls, and operating requirements.

Articles mainly focused on DeepSeek’s model family, platform,
deployment, or development ecosystem should use our

DeepSeek Write for Us

page.

Anthropic vs. DeepSeek R1

A comparison involving DeepSeek R1 should clearly identify the exact
R1 model or deployment being tested. A hosted service, API endpoint,
local model, quantized build, and third-party implementation may produce
different results.

Contributions specifically about R1 reasoning, local deployment,
distillation, evaluation, inference requirements, or implementation
should use our

DeepSeek R1 Write for Us

page.

Anthropic vs. Gemini

Anthropic and Google’s Gemini ecosystem can be compared across model
capabilities, APIs, multimodal input, cloud integration, productivity
workflows, coding, context handling, tool use, safety controls, and
enterprise administration.

Articles focused primarily on Gemini models, Gemini applications,
Google AI development tools, or Gemini integrations should use our

Gemini Write for Us

page.

Anthropic vs. Mistral AI

Comparisons with Mistral AI may examine proprietary and open-weight
deployment options, model size, local or private infrastructure,
multilingual capability, APIs, coding, cost, latency, licences, and
operational control.

Contributions primarily about Mistral models, APIs, self-hosting, model
licences, fine-tuning, or deployment should use our

Mistral AI Write for Us

page.

Anthropic vs. Qwen

Anthropic and the Qwen model family may be compared across language
coverage, coding, reasoning, multimodal tasks, model access,
open-weight options, deployment, API availability, hardware
requirements, and licensing.

Articles mainly covering Qwen models, fine-tuning, local inference,
deployment, evaluation, or its wider ecosystem should use our

Qwen AI Write for Us

page.

How to Compare Anthropic with Other AI Providers

A fair comparison should be designed around a specific user need. Avoid
declaring one provider “best” based on unrelated benchmarks or a small
number of selected prompts.

A comparison should document:

• The exact model and API identifiers
• The date and region of testing
• The task and intended audience
• Prompts, system instructions and tools
• Sampling and output settings
• Evaluation criteria and human reviewers
• Latency, token use and pricing context
• Failures, refusals and inconsistent results
• Privacy, hosting and deployment differences
• Limitations of the comparison

What Makes a Strong Anthropic Article?

A strong contribution is based on first-hand testing, reproducible
development, technical expertise, original evaluation, or a carefully
documented implementation.

Your submission should:

• Identify the Anthropic product and model used.
• State the API, SDK, platform and relevant versions.
• Explain the problem and intended users.
• Describe prompts, tools, data and integrations.
• Document the evaluation method.
• Include failures and limitations.
• Address privacy, security and human oversight.
• Disclose any connection to Anthropic or a compared provider.

Content We Are Unlikely to Accept

We generally do not accept copied Claude feature lists, unsupported
“best AI model” claims, promotional company profiles, outdated model
comparisons, or articles based on a few selected responses.

We may also reject fabricated benchmarks, untested code, collections of
search terms, speculative claims presented as facts, or articles that
ignore privacy, security, cost, model limitations, and human review.

Editorial Guidelines

• Submit original content that has not been published elsewhere.
• Write at least 800 words for a standard contribution.
• Use a focused title, clear headings and short paragraphs.
• Identify exact models, products and testing dates.
• Support technical claims with official documentation or research.
• Use first-hand testing for tutorials and comparisons.
• Do not expose API keys, private prompts or confidential data.
• Disclose relevant employer, client or vendor relationships.
• Avoid promotional, exaggerated or keyword-focused writing.
• Test code and instructions before submission.

AI tools may assist with research, coding or drafting, but authors
remain responsible for verifying every claim, source, benchmark, code
example, model identifier and recommendation. Unedited or unverified
AI-generated submissions will not be accepted.

How to Submit an Anthropic Article

Send your proposal or completed article to

contact@computertechreviews.com
.

Please include:

• Your proposed article title
• A short summary of the article
• The intended audience and use case
• A proposed outline
• The models, products or APIs involved
• Details of your testing or implementation experience
• Links to previous writing samples, if available
• Disclosure of any connected provider, company or client

Related AI Model Contributor Pages

Select the page that most accurately matches the primary model,
provider, or platform covered by your proposed article.

Frequently Asked Questions

Can I submit a Claude API tutorial?

Yes. Identify the exact model, API or SDK version, dependencies,
environment, date of testing, expected output, and important security or
cost considerations. Test the complete tutorial before submission.

Can I submit an Anthropic model comparison?

Yes. Use equivalent tasks and clearly document the models, prompts,
settings, tools, date, evaluation method, costs, failures, and
limitations.

Can I write about Anthropic’s safety research?

Yes. Base the article on Anthropic’s published research, system
documentation, Constitution, transparency materials, or Responsible
Scaling Policy. Clearly separate Anthropic’s claims from independent
analysis and your own conclusions.

Should a DeepSeek R1 comparison go on the DeepSeek or R1 page?

A broad company or model-family comparison belongs on the DeepSeek
page. An article specifically evaluating R1 reasoning, deployment,
distillation or implementation belongs on the DeepSeek R1 page.