Plain-English AI
Every buzzword, decoded.
Vendors profit from confusion; we'd rather you understood. For each term: what it actually is, when you genuinely need it — and when it's a waste of your money.
- Artificial Intelligence (AI)
- Software that performs tasks which normally require human judgment — reading, classifying, ranking, predicting, drafting. Not magic; pattern recognition at scale.
You need it when: A task is repetitive, high-volume, and follows patterns a person could describe.
Skip it when: The task is rare, high-stakes, and genuinely novel each time — keep humans on it.
- Generative AI
- AI that produces new content — text, summaries, drafts, images — rather than just labeling existing content.
You need it when: Your team spends hours drafting emails, reports, job descriptions, or summaries from source material.
Skip it when: You need guaranteed factual precision with zero review — generated output always needs a human check.
- LLM (Large Language Model)
- The engine behind modern AI assistants: software trained on vast text that can read and write language like a tireless analyst.
You need it when: Documents, resumes, tickets, or contracts need to be read, understood, and acted on at scale.
Skip it when: Your problem is numeric forecasting or optimization — classical methods are often cheaper and better.
- AI Agent
- An AI that can take multi-step actions toward a goal — look things up, fill forms, route work — not just answer a question.
You need it when: A workflow chains many small, same-every-time decisions currently done by a person.
Skip it when: The workflow is short or the decisions are judgment calls — a copilot or simple automation is safer.
- RAG (Retrieval-Augmented Generation)
- A design where the AI answers from your documents — policies, contracts, knowledge base — instead of guessing from general internet training.
You need it when: Answers must be grounded in your company's actual documents, accurately, with sources.
Skip it when: Your questions are general-knowledge and your documents add nothing — a plain assistant suffices.
- Copilot
- AI that assists a human doing their job — drafting, suggesting, summarizing — while the human stays fully in charge of the outcome.
You need it when: You want productivity gains without transferring decisions to a machine. The safest first step for most teams.
Skip it when: The task is fully mechanical with no judgment — full automation may be simpler.
- Machine Learning (ML)
- The broader family of techniques where software learns patterns from historical data — powering everything from spam filters to candidate ranking.
You need it when: You have meaningful historical data and a repeating decision to improve (pricing, matching, scoring).
Skip it when: You have little data or the rules are simple enough to just write down.
- Automation vs. AI
- Automation follows fixed rules ('if invoice > ₹50,000, route to CFO'). AI handles variation and judgment ('read this invoice and extract the terms'). Most real systems combine both.
You need it when: Know which you need: rule-describable steps → automation; messy, human-language inputs → AI.
Skip it when: Don't buy AI for a problem a rule can solve — rules are cheaper, faster, and easier to audit.
- Hallucination
- When generative AI states something false with confidence. It isn't lying — it's completing patterns without checking facts.
You need it when: Understand this before deploying any generative AI: it's why human review gates and RAG grounding exist.
- Human-in-the-loop
- System design where AI drafts, ranks, or recommends, but a named person approves before anything consequential happens.
You need it when: Always, for decisions about people, money, or compliance. It's non-negotiable in every Hab deployment.
Skip it when: Trivial, reversible actions (e.g., tagging tickets) can often run unattended once proven.
- AI Governance
- The rules, roles, and records around your AI: who owns each decision, how outputs are audited, what data flows where, and how errors are caught and corrected.
You need it when: The moment AI touches decisions about people or money — and before procurement or regulators ask.
- DPDP Act
- India's Digital Personal Data Protection Act — the law governing how organizations collect, process, and store personal data, including in AI systems.
You need it when: Any AI system touching personal data in India (resumes very much included) should be designed with DPDP awareness.
Now you speak the language. Let's talk process.
The glossary tells you what the tools are. A strategy call tells you which one — if any — your business actually needs.
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