AI Problem Solver
Common AI Development Issues — Solved
Real answers to the problems that slow down AI development — from API issues and GDPR compliance to automation failures and adoption strategy. If your issue isn't here, get in touch.
AI API & Integration
Why is my Claude API returning rate limit errors in production?
Claude API rate limits apply per-minute and per-day by tier. In production, implement exponential backoff with jitter, cache responses where appropriate, and use a queue for batch operations. Consider upgrading your Anthropic tier or distributing load across multiple API keys with a provider abstraction layer.
How do I stop my AI giving inconsistent or hallucinated output?
Hallucination reduces significantly with structured output prompting (ask for JSON with a defined schema), temperature set to 0 for factual tasks, few-shot examples showing correct output format, and output validation with retry logic. Never blindly trust AI JSON — validate the schema before using the response.
How do I prevent prompt injection in my AI application?
Separate system instructions from user content using clear structural boundaries. Never concatenate user input directly into system prompts. Validate and sanitise all external content before it enters your prompt. Use a content safety layer to detect injection patterns. Treat external content — documents, URLs, emails — as untrusted input.
My AI output is too slow for a good user experience. How do I fix it?
Use streaming responses to show output as it generates rather than waiting for completion. Cache repeated or similar requests. Choose a faster model tier for simple tasks — economy models often perform just as well as premium for structured generation. Run AI calls server-side to avoid client-side latency.
How do I choose between Claude API, OpenAI and Gemini for my project?
Claude API excels at long-context reasoning, document analysis and following complex instructions. OpenAI GPT models have the broadest ecosystem and tooling. Gemini is strong on multimodal and Google integration. For production, build an AI gateway abstraction so you can switch providers without rewriting application code — provider lock-in is a real risk.
GDPR & Data Security
Is my AI application GDPR compliant?
GDPR compliance for AI applications requires: a lawful basis for processing (consent or legitimate interest), data minimisation (only collect what you need), clear privacy policy, right to deletion, no transfer of personal data to AI providers without a Data Processing Agreement (DPA). Anthropic, OpenAI and Google all offer DPAs. Check your AI provider's data retention policy — some train on user data by default unless you opt out.
How do I build an AI system that doesn't store personal data?
Design your prompt architecture to strip or anonymise personal identifiers before they reach the AI API. Use pseudonymisation — replace names and emails with tokens before processing, restore after. Implement data retention limits and automated deletion. Log AI requests without personal content. Use in-context processing where possible rather than storing data in a vector database.
What should I put in a privacy policy for an AI application?
Your privacy policy must cover: what data is collected, why it is processed (lawful basis), how long it is retained, whether it is shared with AI providers (name them — Anthropic, OpenAI etc.), user rights (access, deletion, portability), DPA status with your AI provider, ICO registration number (UK), and a contact for data queries.
How do I handle user consent for AI processing of their data?
Use granular, informed consent — explain specifically that an AI will process their input. Pre-ticked boxes are not valid GDPR consent. Store consent records with timestamp and version. Provide an easy way to withdraw consent. If using AI for automated decision-making that significantly affects users, you must inform them and offer a human review option under Article 22.
n8n & Workflow Automation
My n8n workflow is failing silently. How do I debug it?
Enable execution logging in n8n settings. Add Error Trigger nodes to catch failures and send notifications. Use Set nodes to log intermediate values during development. Check your n8n instance timezone settings if scheduled triggers are misfiring. For webhook triggers, inspect the incoming payload structure — mismatched field names cause silent failures.
When should I use n8n instead of custom code for AI automation?
Use n8n when you need to connect established services (Gmail, Slack, CRMs, databases) with minimal custom logic. Use custom code when you need complex data transformation, custom AI prompt chains, financial-grade reliability, or when the workflow logic will exceed n8n's node complexity. For AI-heavy workflows, n8n handles orchestration while custom code handles the AI generation layer.
AI Adoption & Strategy
How do I know if AI is the right solution for my business problem?
AI is a good fit when: the task involves language understanding or generation, pattern recognition in large datasets, personalisation at scale, or automating judgment-based repetitive work. AI is not the right tool for: precise calculations (use code), problems with no data, tasks requiring 100% accuracy with no tolerance for error, or problems that a simple rule-based system would solve.
How much does it cost to adopt AI in a small business?
AI adoption costs range from £0 (using free tiers of ChatGPT or Claude for internal tasks) to £1,500–£5,000+ for a custom AI application. AI API costs for a production application typically run £50–£500/month depending on volume. The bigger cost is usually development time, not API fees. An AI audit (from £500) can identify where AI will create the most value before you commit to a build.
What is the difference between an AI agent and AI automation?
AI automation executes a defined sequence of steps triggered by an event — reliable, predictable, rule-based. An AI agent perceives its environment, makes decisions and takes actions to achieve a goal — flexible, can handle unexpected inputs, but requires more careful design and testing. Most business problems start with automation; agents add value when the task requires judgment or varies significantly.
How long does it take to build an AI product for my business?
A focused AI prototype demonstrating a single workflow: 1–2 weeks. A working MVP with authentication, database and 2–3 core features: 4–6 weeks. A production-ready application with security, GDPR compliance, deployment and operations: 6–12 weeks. Timelines depend on integration complexity, data availability and how clearly the requirements are defined upfront.
Your issue isn't here?
If you're stuck on an AI development problem, a GDPR compliance question or an automation failure, reach out. We can help — or point you in the right direction.