Translate Technical Claims
Decode the 12 technical terms you'll hear in every vendor pitch. From APIs to LLMs, understand what they really mean and what red flags to watch for.
Vendors love technical jargon. It makes simple things sound complex and complex things sound magical. Here are the 12 terms you'll hear most — translated into plain English.
1. API (Application Programming Interface) What it means: Two tools talking to each other automatically. Cost context: Custom integrations run $5K–$25K and take 2–6 weeks. Red flag: "Native integration" sometimes means expensive custom API work.
2. Rate Limits What it means: Speed limits on data transfer — like Netflix limiting you to 4 simultaneous streams. Why you care: Rate limits can break campaigns mid-flight. Always ask about hidden overage costs.
3. Webhook What it means: An automated notification trigger. When X happens in Tool A, Tool B instantly reacts. The risk: Webhooks can fail silently. Ask if there are failure notifications.
4. Machine Learning vs "AI" Machine Learning: A system that learns patterns from historical data rather than following explicit rules. AI: A broad umbrella term that could mean anything. Red flag: Vendors frequently call basic if/then rules "AI."
5. Token What it means: Chunks of text that AI processes. Roughly 750 words equals 1,000 tokens. Both your input and the AI's output count toward costs. Overages typically run $0.01–$0.10 per 1K tokens.
6. Latency What it means: Response delay time. 200ms feels instant, 2,000ms feels slow. High latency breaks user experience. Red flag: No SLA (service-level agreement) for response times.
7. Training Data What it means: The examples a model learned from. The quality and relevance of training data determines performance. Red flag: "Industry-specific training" could mean almost anything.
8. Model Fine-Tuning What it means: Further training a pre-built model on your specific data. Costs $5K–$50K+ and typically requires 1,000+ examples. Red flag: Some vendors call simple prompt customization "fine-tuning."
9. Prompt Engineering What it means: The art of writing instructions for AI to get the desired output. Bad prompts produce inconsistent results. Red flag: Vendors hiding behind "you need better prompts" when their product underperforms.
10. Vector Database What it means: A database that stores information by meaning, not keywords. Enables semantic search and smart recommendations. Red flag: Expensive to scale — ask about storage costs at volume.
11. LLM (Large Language Model) What it means: The AI brain powering tools like ChatGPT and Claude. Building one from scratch costs $10M–$100M+. The reality: most vendors use OpenAI or Anthropic with custom prompts on top. Red flag: "Proprietary LLM" is rarely true.
12. Embeddings What it means: Converting text into numerical representations that capture meaning. Enables semantic search and content recommendations. Costs vary by embedding model and scale.
KEY TAKEAWAY
You don't need to become an engineer. You need to know enough to ask the right questions. When a vendor throws jargon at you, refer back to these definitions and ask them to explain in plain terms. If they can't — or won't — that's a red flag on its own.