How the Use Case Gallery Works
The Claude Use Case Gallery is a browsable library of implementation patterns for building applications with Claude. Each use case includes a description of the problem it solves, the recommended architecture, the suggested model, an estimated cost range, and an implementation checklist. The gallery is designed for developers and product managers who know they want to use Claude but are not sure which pattern fits their needs. Instead of starting from scratch, find a similar use case in the gallery and adapt its architecture to your specific requirements.
Filter by industry to see use cases relevant to your domain. Filter by type to focus on chatbots, pipelines, agents, automation, or analysis patterns. Filter by complexity to match your team's experience level. Click any use case card to see its full architecture diagram and implementation details. The architecture diagrams show the key components and how data flows between them, from user input through Claude API calls to the final output. These diagrams are intentionally simple, showing the conceptual architecture rather than deployment specifics, so you can implement them with any technology stack.
Common Architecture Patterns
The direct API integration pattern is the simplest and most common architecture. The user's input is sent to Claude along with a system prompt, and the response is returned directly. This pattern works for chatbots, content generators, code assistants, and any use case where the model has enough knowledge in its training data to answer without external context. The architecture is: User Interface to Backend Server to Claude API and back. Implementation takes hours, not weeks. The main design decisions are the system prompt, the model choice, and conversation history management.
The RAG (Retrieval-Augmented Generation) pipeline pattern adds an external knowledge base to the architecture. Before calling Claude, the system searches a vector database for documents relevant to the user's query. The retrieved documents are injected into the prompt as context. This pattern is essential for customer support bots that reference help articles, internal assistants that query company documentation, and any application where the model needs information not in its training data. The architecture adds a vector database and retrieval layer between the user input and the Claude API call.
The multi-agent orchestration pattern uses multiple Claude instances with different roles. A supervisor agent coordinates the workflow, delegating subtasks to specialized worker agents. Each agent has its own system prompt and potentially a different model. This pattern handles complex workflows like code review (separate agents for security, style, performance), content creation (research, draft, edit, format), and research synthesis (data collection, analysis, report generation). The architecture is more complex but produces significantly better results for tasks that benefit from specialized attention to different aspects.
The automation pipeline pattern chains Claude calls together in a sequence without direct user interaction. A trigger (webhook, scheduled job, or file upload) initiates the pipeline. Each stage processes data, potentially transforming it, and passes the result to the next stage. This pattern works for document processing (ingest, extract, classify, store), content moderation (analyze, flag, route, action), and data enrichment (receive, research, augment, validate). The key architectural consideration is error handling between stages: if stage 3 of 5 fails, do you retry, skip, or abort the pipeline.
Industry-Specific Patterns
SaaS applications most commonly use Claude for in-app assistants that help users navigate complex features, for automated customer support that handles Tier 1 queries, and for content generation that produces onboarding emails, product descriptions, and help articles. The typical architecture is a direct API integration with RAG over the product's help documentation. Cost is usually $50 to $500 per month depending on user volume. The critical success factor is the system prompt, which must teach Claude about the specific product's features and terminology without being so long that it consumes excessive tokens.
Fintech applications leverage Claude for regulatory document analysis, transaction monitoring narrative generation, customer communication drafting, and risk assessment summaries. These applications typically require higher accuracy than other industries because errors have financial and legal consequences. Use Claude 4 Opus for compliance-sensitive tasks and implement multi-layer validation to catch hallucinations. RAG over regulatory databases is common. Cost is higher due to Opus pricing but justified by the value of accurate regulatory analysis that would otherwise require expensive human analysts.
Healthcare applications use Claude for clinical note summarization, patient communication drafting, medical literature synthesis, and administrative workflow automation. HIPAA compliance is a primary architectural constraint. All Claude API calls must go through BAA-covered infrastructure. Use Anthropic's enterprise tier with a signed BAA for healthcare deployments. The system prompt must include explicit instructions about not providing medical diagnoses or treatment recommendations. RAG over medical literature databases provides the clinical context the model needs for accurate summarization.
Education applications deploy Claude as tutoring assistants, essay feedback generators, quiz creators, and curriculum development tools. The system prompt for educational use cases must balance being helpful with not doing the student's work. Socratic prompting instructions that guide students toward answers through questions rather than providing direct answers produce the best educational outcomes. Cost is typically low because student interactions are short and can often use Claude 3.5 Haiku. The main challenge is maintaining pedagogical quality across diverse subjects and student skill levels.
Cost Estimation by Use Case
Customer support chatbots handling 1,000 conversations per day with 5 turns each, using Claude 3.5 Haiku, cost approximately $5 to $20 per month. Switching to Claude 3.5 Sonnet for better quality raises cost to $50 to $200 per month. Adding RAG over a knowledge base increases cost by 20 to 30 percent due to longer prompts. The ROI is clear: deflecting even 30% of support tickets at an average handling cost of $5 to $15 per ticket saves $45,000 to $135,000 per month for a 1,000 ticket per day operation.
Code review assistants analyzing 50 pull requests per day with an average of 500 lines of code per PR, using Claude 3.5 Sonnet, cost approximately $30 to $100 per month. Multi-agent review (security, style, performance agents) costs 3 to 5 times more but catches significantly more issues. The architecture is typically a webhook triggered by PR events that sends the diff to Claude with a code review system prompt and posts comments back to the PR. Integration with GitHub Actions or GitLab CI makes the process fully automated.
Document processing pipelines that extract structured data from unstructured documents, such as invoices, contracts, or reports, cost approximately $0.01 to $0.05 per document depending on document length and extraction complexity. At 10,000 documents per day, monthly cost ranges from $3,000 to $15,000. Using Claude 3.5 Haiku for extraction can reduce costs to $300 to $1,500 per month for the same volume. The architecture typically involves OCR preprocessing, chunking for long documents, Claude extraction, and validation against expected schemas.
Privacy and Local Execution
The Claude Use Case Gallery runs entirely in your browser. Filtering, searching, and viewing architecture details are processed client-side using JavaScript. No data is sent to any server. There are no accounts, no cookies, no analytics, and no server-side processing. The gallery is a reference tool that helps you plan your Claude implementation before writing code.