Questions, answered.
Long-form answers to the questions teams actually ask before they put AI in production — agents, RAG, support, evaluation, and how an implementation runs. Each page is built to be cited, not skimmed.
RAG FAQ
Answers on retrieval-augmented generation: how RAG differs from fine-tuning, why it still hallucinates, how to evaluate it, and how permissions work at
Customer Support AI FAQ
How support AI actually works: deflection without hiding bad experiences, grounded policy answers, order actions, escalation, and a four-week path to live
LLM Evaluation FAQ
How production LLM evaluation actually works: golden datasets, rubric judges, regression gates, online monitors, and how you catch a silent model update.
Implementation, Security & Governance FAQ
How a ReinforcedX implementation actually runs: four weeks to a first agent, who owns the IP, where data lives, what security review looks like, and what
Agentic AI FAQ
What agentic AI is versus generative chat, how agentic workflows differ from RPA and copilots, loop limits, and when a single model call is the better
AI Guardrails FAQ
What AI guardrails are, how they differ from provider safety filters, false positives, latency, fail-closed vs fail-open, and how to implement them on
ChatGPT for Business FAQ
Can your company use ChatGPT? Consumer vs Team vs Enterprise vs the API vs a custom agent. ReinforcedX does not resell ChatGPT; we implement production
Computer-Use Agents FAQ
What computer-use and browser agents are, when to prefer an API, how to sandbox clicks, and when a UI-level agent is the wrong buy.
AI Copilot vs Agent vs Chatbot FAQ
The taxonomy buyers actually search: chatbot, copilot, agent, and RPA — what each is, when to buy which, and when a copilot project should become an agent.
Embeddings FAQ
What text embeddings are, how they power search and RAG, when to re-embed, whether you need a vector database, and how domain models fit.
EU AI Act and Generative AI FAQ
Plain answers on the EU AI Act for generative AI teams: GPAI versus high-risk, logging, transparency, human oversight. Not legal advice — engineering
LLM Fine-Tuning FAQ
What LLM fine-tuning is, when RAG is enough, PEFT and LoRA, data needs, ownership of weights, and why most teams should not fine-tune ChatGPT first.
Generative AI FAQ
What generative AI is, how it differs from traditional ML, the production risks, and how teams ship a first system inside their own cloud in four weeks.
AI Hallucinations FAQ
What AI hallucinations are, why ChatGPT invents facts, how to detect them, and the production controls — retrieval, refusal, citations, evals — that
LLM Cost FAQ
How LLM API cost actually works: input vs output tokens, retries, judges, caching, routing, and why the bill exceeds the playground — without stale list
LLM Data Privacy FAQ
Is ChatGPT private? Can you put company data in it? GDPR, zero data retention, VPC, PII in logs, and the honest limits of “the model forgets.”
Model Context Protocol (MCP) FAQ
What MCP is, how MCP servers differ from native function calling, the security risks, when to use a gateway, and when not to wrap every API as MCP.
Open-Source LLMs FAQ
When open-weight models win on residency, cost, and control; when frontier APIs still win; licensing caveats; and how to self-host without turning GPUs
Prompt Engineering FAQ
What prompt engineering is, why evals beat prompt folklore, how system vs user vs tool prompts differ, and when to stop prompting and fine-tune or
Reasoning Models FAQ
What reasoning or “thinking” models are, how they differ from chat models, why they cost more, and when they help an agent — or just add latency.
Structured Outputs and JSON Mode FAQ
How to force LLMs to return JSON: structured outputs, JSON mode, constrained decoding, retries, and why agents still need a schema registry.
Synthetic Data for AI FAQ
When synthetic data helps LLM training and evaluation, when it collapses, whether it is legal, and how to keep real PII out of generated sets.
Vector Databases FAQ
What a vector database is, whether you need one for RAG, Pinecone vs Weaviate vs pgvector, hybrid search, ACLs, and when Postgres is enough.
Voice AI FAQ
What voice AI is, how a voice agent differs from IVR, the latency budget that keeps callers on the line, barge-in, disclosure, and when not to use it.
Each topic ships as its own page with FAQ schema and stable fragment ids, so you can cite a single answer rather than the whole hub.
In short
These hubs answer the questions people actually search: what generative AI is, how an agent differs from a copilot, why models hallucinate, when to fine-tune versus retrieve, how MCP and guardrails work, and what a four-week implementation actually contains.
- Topics
- 25 long-form hubs
- Depth
- 625 questions in all
- Written by
- The team that ships the work
Which FAQ to open first
Start with the symptom you actually have. Each hub is a topic, not a dump of every question on the site.
| Kind | Questions | Open it when | |
|---|---|---|---|
| AI Agents FAQ | Use case | 25 | A chatbot produces a reply. An agent retrieves context, calls tools, and changes state in systems you already run — under permissions you issue. |
| RAG FAQ | Use case | 25 | Fine-tuning changes how the model talks. RAG changes what it is allowed to know today. Most knowledge problems are RAG problems; most style problems are not. |
| Customer Support AI FAQ | Domain | 25 | The normal shape is agents taking repetitive volume and escalating the rest. Support orgs that try to remove humans in month one usually put them back in month two. |
| LLM Evaluation FAQ | Quality | 25 | Quality is a score on a golden set plus a live sample. If you cannot fail a release, you do not have evaluation — you have a vibe. |
| Implementation, Security & Governance FAQ | Delivery | 25 | Consultancies bill for the roadmap and leave you the build. Every engagement ends with running software, an eval suite, and a runbook your team owns. |
| Agentic AI FAQ | Use case | 25 | Agentic means plan–act–observe under a stop condition. Buying “agentic AI” without a workflow is buying a word. |
| AI Guardrails FAQ | Quality | 25 | A system prompt is a request. A guardrail is a check that can block, rewrite, or escalate regardless of what the model wanted to say. |
| ChatGPT for Business FAQ | Use case | 25 | Consumer app, Team, Enterprise, and API. They do not share privacy, admin, or integration depth. Saying “we use ChatGPT at work” without which one is how incidents start. |
| Computer-Use Agents FAQ | Use case | 25 | If the system has an API, a function-calling agent is cheaper, faster, and easier to audit. Computer use exists for the residue: vendor portals, desktop clients, and tools that will not give you a key. |
| AI Copilot vs Agent vs Chatbot FAQ | Use case | 25 | If a person must approve every external action, you are buying a copilot. If the system may change state on its own under policy, you are buying an agent. If it only answers, it is a chatbot. |
| Embeddings FAQ | LLM Technique | 25 | Embeddings find nearby passages. A chat model still has to read them. A beautiful index with an ungrounded generator is still a hallucination machine. |
| EU AI Act and Generative AI FAQ | Governance | 25 | High-risk is a use-case classification, not a model-size classification. Support, internal Q&A, and drafting tools usually sit in transparency and logging, unless they are used for Annex III decisions such as hiring or credit. |
| LLM Fine-Tuning FAQ | LLM Technique | 25 | Fine-tuning teaches a behaviour. RAG supplies current documents. Putting the wiki into a training set is how you bake in last quarter’s policy. |
| Generative AI FAQ | Foundation | 25 | Traditional ML maps an input to a label or a score. Generative AI samples new text, code, or images. That flexibility is the point and the risk. |
| AI Hallucinations FAQ | Quality | 25 | A hallucination is a fluent claim without support in the retrieved sources or tool results. Intent is the wrong frame; sampling is the mechanism. |
| LLM Cost FAQ | Infrastructure | 25 | Providers bill input and output tokens at different rates. Output and “thinking” tokens usually dominate completions; RAG context dominates inputs. |
| LLM Data Privacy FAQ | Governance | 25 | chatgpt.com on a personal account is the wrong place for source code, customer PII, or unreleased numbers. Employees will still paste. Technical controls beat a policy PDF. |
| Model Context Protocol (MCP) FAQ | Infrastructure | 25 | MCP does not make the model smarter. It standardises how tools and resources are discovered and called. |
| Open-Source LLMs FAQ | Foundation | 25 | Self-host when data cannot leave the perimeter, when you must pin a checkpoint, or when you need an adapter you actually own. |
| Prompt Engineering FAQ | LLM Technique | 25 | A bad system prompt will sink a good model. A clever prompt without a golden set is folklore. Ship both. |
| Reasoning Models FAQ | Foundation | 25 | Extra search at inference time raises the odds on contest-style problems. It does not make retrieved policy true, and it does not replace citations or tools. |
| Structured Outputs and JSON Mode FAQ | LLM Technique | 25 | Asking for JSON, or flipping a JSON-mode flag, still yields trailing text, wrong types, and missing keys. Validate. Do not parse with optimism. |
| Synthetic Data for AI FAQ | Training | 25 | Synthetic records inherit the teacher model’s license, the seed examples you fed it, and any PII those seeds contained. “We made it up” is not a privacy or copyright defence. |
| Vector Databases FAQ | Infrastructure | 25 | Documents still live in Drive, SharePoint, or the warehouse. The vector store is a derived index. Treat it like a search index, not like a system of record. |
| Voice AI FAQ | Domain | 25 | IVR walks a tree. A voice agent extracts a structured object — intent, slots, next action — and can call tools. The conversation order is the model’s job; the schema is yours. |
Questions people ask about this library
What is this FAQ for?
Long-form answers to the questions people search before they build production AI — generative AI, agents versus copilots, RAG, hallucinations, fine-tuning, MCP, cost, privacy, and how an implementation actually runs. Each page is a topic, not a dump of every question on the site.
How is this different from the questions on product pages?
Product pages answer the ten questions that page raises. These hubs go deeper — twenty-plus questions, with domain and use-case questions called out, written so a person or an answer engine can cite a single answer.
Can I link to a single answer?
Yes. Every question has its own heading id, and each page has key pointers with stable fragments, so you can link a specific claim in a doc or a ticket.
Who writes these?
The delivery team, against the same claims the rest of the site already makes. If an answer is not true of an engagement we would actually run, it does not go in.
Which FAQ should we start with?
Generative AI if you need the definition. Copilot vs Agent if you are choosing a shape. Hallucinations if answers invent facts. Fine-tuning if you are choosing RAG versus weights. Implementation if legal, security, or procurement is the next conversation.
Are the numbers real?
Yes, and they match what we already publish: four-week implementations, you own the IP, data stays in your perimeter. Sector-specific ranges (deflection, weeks to a governed pilot) come from the consulting and case-study pages.
Can we quote these answers?
Yes, with attribution to ReinforcedX and a link back to the question. They are written to be quoted.
Do you keep them updated?
When the underlying engagement changes. A FAQ that no longer matches how we work is worse than no FAQ.
What if our question is not here?
Book a working session and ask it. If several people ask the same thing it tends to become an entry.
Can you just build the thing instead of us reading this?
Yes. That is the day job. The FAQs exist because plenty of teams need the answers in writing before they can get a meeting on the calendar.