AI & Technology
Articles about AI & Technology - insights and guides for better customer support.
What this ai & technology category is for
This hub is for readers who want practical, implementation-focused ai & technology guidance for AI customer support. It is not a generic archive. Each article should help you make a decision, set up a workflow, compare a tradeoff, or improve a measurable support outcome.
Start by choosing the question closest to your current problem. If you are still researching, read the newest overview first. If you are already implementing, prioritize articles that include checklists, examples, benchmarks, or setup steps. If you are measuring results, pair the article with the ROI calculator, response benchmark, or chatbot templates.
How to evaluate a customer support chatbot before launch using a 25-question golden set, four scoring dimensions, and the right tooling stack.
Prompt injection is the #1 OWASP GenAI risk. Air Canada, DPD, Microsoft, and Bing all got bitten. Here is what actually defends a support chatbot in 2026.
Claude 4.5 has 200k context. Gemini 2.5 hits 2M. Some teams are killing RAG and stuffing everything in. Here is when that actually works for support.
All AI & Technology Articles
AI Chatbot Evaluation and Testing: A Support Team Playbook for 2026
How to evaluate a customer support chatbot before launch using a 25-question golden set, four scoring dimensions, and the right tooling stack.
AI Chatbot Prompt Injection Defenses in 2026: What Works, What Does Not
Prompt injection is the #1 OWASP GenAI risk. Air Canada, DPD, Microsoft, and Bing all got bitten. Here is what actually defends a support chatbot in 2026.
Long Context vs RAG for Customer Support in 2026: When Stuffing the Prompt Beats Retrieval
Claude 4.5 has 200k context. Gemini 2.5 hits 2M. Some teams are killing RAG and stuffing everything in. Here is when that actually works for support.
Multimodal AI in Customer Support: What's Real in 2026
Vision, voice, and screen-sharing models are in production for support. Here's what actually works, what is still hype, and what it costs per interaction.
AI Agents vs Chatbots in 2026: What's the Real Difference?
Chatbots respond. Agents take action. The terminology is a mess. Here's how to tell them apart and pick the right one for your use case.
AI Search vs Traditional Search for Help Centers: Which Wins in 2026
Semantic AI search vs keyword search for docs and support. Real costs, latency, and the four scenarios where each one wins.
Browser-Using AI Agents for Customer Support: What They Actually Do in 2026
Claude Computer Use, OpenAI Operator, browser-use, Browserbase. Real prices, real speeds, real support use cases, and where they fail today.
MCP for Customer Support: What It Is and How to Use It in 2026
Model Context Protocol turns 18 months old and finally has enough servers to matter. Here's how support teams should actually use it.
Voice AI for Customer Support in 2026: What Works, What Doesn't
Voice agents are real and growing fast. Most workflows still belong on chat. Here's the honest map of what voice AI can and can't do today.
How to use these ai & technology resources
This category collects practical Chatsy articles for teams evaluating AI customer support in 2026. Use it as a focused research path: start with the highest-level guide, compare the operational tradeoffs, then move into implementation steps before changing your live support workflow.
The strongest launch plan usually has 3 parts: a documented knowledge base, clear escalation rules for human takeover, and a recurring measurement loop for unresolved questions, first response time, containment, and customer satisfaction. Those checkpoints keep automation useful instead of turning it into a thin FAQ bot.
For adjacent planning, review the AI chatbot features, compare rollout costs with the ROI calculator, and start implementation from a chatbot template.
If this category has only a few articles today, treat the page as a launch map rather than a simple archive. Read the newest post first, then move into related templates, tools, and solution pages so the topic is tied back to a complete support workflow. That structure helps teams move from research to implementation without losing the operational details that make AI support reliable.
Before acting on any recommendation, write down your current baseline for 5 numbers: monthly tickets, first response time, average resolution time, escalation rate, and customer satisfaction. Revisit those numbers after 30 days so the category becomes a measurable improvement path, not just reading material.
Small categories still deserve useful context. If only a handful of posts exist, use the focus areas below to decide what to read next, what to test inside Chatsy, and which internal page should support the topic. That keeps the archive useful for searchers even before the editorial cluster grows.
As the cluster expands, add articles that answer one precise question at a time so the category stays easy to scan and genuinely useful.
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