A useful AI workflow has three parts: the model, the information and tools it can use, and checks around the output. A larger context window does not make stale or irrelevant context useful. Retrieval can bring current material into a response, but grounding is only as good as the retrieved evidence. Treat prompts as instructions, not security boundaries: untrusted content can attempt prompt injection. Evaluate the complete workflow on representative cases, define what humans must review, and monitor for drift after launch.
🧭 Jump to a term
Models and inputs: LLM · Multimodal model · Prompt · Context window · Token
Grounding and reliability: RAG · Grounding · Hallucination · Fine-tuning · Prompt injection
Controls and operations: Model evaluation · HITL · Structured output · Batch inference · Model drift · Synthetic data
⭐ Know these first
Start with LLM, RAG, Hallucination, Prompt injection, HITL. Then follow the grouped learning order below.
📎 How to read this page
What it means gives the precise meaning. Operator translation gives the version you might hear in a real ecommerce meeting. In real life shows an illustrative example. Watch out and the confusion boxes show where a familiar term can mislead.
📈 Read the relationships first
These combinations are diagnostic hypotheses, not proof of causality. Compare the same period and scope, then investigate the mechanism.
More retrieved context, fewer correct answers
Usually means: The added material may be irrelevant or contradictory. Check next: retrieval precision and whether the prompt distinguishes sources.
Great demo, weak evaluation score
Usually means: The demo may use a narrow or hand-picked case. Check next: a representative test set and the cost of failures.
Structured output parses, decisions still fail
Usually means: Valid formatting does not guarantee correct content. Check next: field-level rules and downstream validation.
Automation volume rises, exceptions rise too
Usually means: The workflow may be amplifying a failure mode. Check next: drift, fallback handling and human review thresholds.
Models and inputs
01 · 🟢 Core
LLM = Large language model
🧠 What it means
A model architecture trained to predict and generate sequences of language tokens.
💬 OPERATOR TRANSLATION
“The text predictor that can write a draft, not the colleague who owns the decision.”
🛍️ In real life
A support team drafts replies with a model, then checks policy-sensitive cases.
🔗 Related: Multimodal model · Prompt · Context window · ↑ all terms
02 · 🔵 Operations
Multimodal model
🧠 What it means
A model that can process more than one data type, such as text and images.
💬 OPERATOR TRANSLATION
“It can read more than prose, though it can still miss what the photo shows.”
🛍️ In real life
A workflow reads a product photo and a written return request together.
🔗 Related: LLM · Prompt · Context window · ↑ all terms
03 · 🔵 Operations
Prompt
🧠 What it means
The input that guides a model’s response, including instructions, context and requested output.
💬 OPERATOR TRANSLATION
“The instructions handed to the model; specificity helps, certainty does not.”
🛍️ In real life
A prompt asks for three compatible accessories and requires evidence for each.
🔗 Related: LLM · Multimodal model · Context window · ↑ all terms
04 · 🔵 Operations
Context window
🧠 What it means
The bounded amount of input and output a model can process in one request.
💬 OPERATOR TRANSLATION
“The request’s working memory limit, not a promise that every included detail matters.”
🛍️ In real life
A long policy file exceeds the model’s request limit and must be summarized or retrieved in parts.
🔗 Related: LLM · Multimodal model · Prompt · ↑ all terms
05 · 🔵 Operations
Token
🧠 What it means
A unit of text or other data used by a model; tokenization does not map one-to-one to words.
💬 OPERATOR TRANSLATION
“The model’s counting unit; a word and a token are not always twins.”
🛍️ In real life
A short-looking multilingual prompt consumes more tokens than expected.
🔗 Related: LLM · Multimodal model · Prompt · ↑ all terms
Grounding and reliability
06 · 🟢 Core
RAG = Retrieval-augmented generation
🧠 What it means
A workflow that retrieves relevant material and gives it to a model to inform its response.
💬 OPERATOR TRANSLATION
“Retrieve the right documents first, then ask the model to use them.”
🛍️ In real life
A returns assistant retrieves the current policy before drafting an answer.
⚠️ Watch out
Retrieval can return stale or irrelevant passages. Keep source dates and access controls visible.
🔗 Related: Grounding · Hallucination · Fine-tuning · ↑ all terms
07 · 🔵 Operations
Grounding
🧠 What it means
Connecting a generated answer to supplied evidence, data or tools. Grounding reduces unsupported claims but cannot guarantee correctness.
💬 OPERATOR TRANSLATION
“Point the answer at evidence; still check whether the evidence is current and complete.”
🛍️ In real life
A support reply cites the order status returned by the order system.
⚠️ Watch out
Grounded output can still misread or overstate its source. Validate consequential facts.
🔗 Related: RAG · Hallucination · Fine-tuning · ↑ all terms
08 · 🟢 Core
Hallucination
🧠 What it means
A plausible-sounding generated statement that is unsupported or incorrect.
💬 OPERATOR TRANSLATION
“A confident sentence with no reliable footing.”
🛍️ In real life
A model invents a return deadline absent from the policy.
⚠️ Watch out
Fluency is not a confidence signal. Escalate when the evidence is missing or conflicts.
🔗 Related: RAG · Grounding · Fine-tuning · ↑ all terms
09 · 🔵 Operations
Fine-tuning
🧠 What it means
Additional training on examples to adapt a model’s behavior for a task or domain.
💬 OPERATOR TRANSLATION
“Teach the model’s behavior with examples; it is not the same as supplying today’s facts.”
🛍️ In real life
A company tunes a model on approved product-support examples.
⚠️ Watch out
Treat retrieved pages, emails and customer text as untrusted input; separate them from system instructions.
🔗 Related: RAG · Grounding · Hallucination · ↑ all terms
10 · 🟢 Core
Prompt injection
🧠 What it means
An attack that places malicious instructions in untrusted input to influence a model or its connected tools.
💬 OPERATOR TRANSLATION
“Untrusted text tries to become the boss of the prompt.”
🛍️ In real life
A product description tells an agent to ignore its rules and reveal private data.
🔗 Related: RAG · Grounding · Hallucination · ↑ all terms
Controls and operations
11 · 🔵 Operations
Model evaluation
🧠 What it means
A systematic check of model or workflow performance against defined examples and criteria.
💬 OPERATOR TRANSLATION
“Put the whole workflow through cases that resemble production, including awkward ones.”
🛍️ In real life
A team scores answers for accuracy, policy adherence and escalation quality.
⚙️ Operations
Route human review by risk and define a fallback for unanswered cases.
🔗 Related: HITL · Structured output · Batch inference · ↑ all terms
12 · 🟢 Core
HITL = Human-in-the-loop
🧠 What it means
A design where a person reviews or approves selected model outputs or actions.
💬 OPERATOR TRANSLATION
“A human check at selected points, ideally where the downside is real.”
🛍️ In real life
A human approves refunds above a chosen threshold.
🔗 Related: Model evaluation · Structured output · Batch inference · ↑ all terms
13 · 🔵 Operations
Structured output
🧠 What it means
An output constrained to a defined schema such as JSON fields and types.
💬 OPERATOR TRANSLATION
“Valid JSON is a good envelope; it does not certify what is inside.”
🛍️ In real life
A classifier returns a category and confidence field for each case.
🔗 Related: Model evaluation · HITL · Batch inference · ↑ all terms
14 · 🔵 Operations
Batch inference
🧠 What it means
Running model requests over many inputs asynchronously or in groups rather than one interactive request at a time.
💬 OPERATOR TRANSLATION
“Send the queue in batches when nobody needs an instant answer.”
🛍️ In real life
A catalog team processes thousands of product descriptions overnight.
🔗 Related: Model evaluation · HITL · Structured output · ↑ all terms
15 · 🔵 Operations
Model drift
🧠 What it means
A change in model or workflow behavior over time as inputs, models or surrounding systems change.
💬 OPERATOR TRANSLATION
“The workflow changed under your feet; the old score may no longer describe it.”
🛍️ In real life
A new product feed format degrades extraction accuracy after launch.
🔗 Related: Model evaluation · HITL · Structured output · ↑ all terms
16 · 🔵 Operations
Synthetic data
🧠 What it means
Artificially generated data used for development, testing or training.
💬 OPERATOR TRANSLATION
“Made-up data for testing; useful for coverage, not a substitute for real-world evidence.”
🛍️ In real life
A team creates synthetic edge cases to test address normalization.
🔗 Related: Model evaluation · HITL · Structured output · ↑ all terms
🔀 Context window and memory
A context window is what a model can process for a request; it does not imply durable memory between requests.
🔀 Grounding and truth
Grounding links a response to provided evidence. The evidence may still be incomplete or wrong.
🔀 Fine-tuning and retrieval
Fine-tuning changes model behavior through training; retrieval supplies information at inference time.
🤔 Still confused?
Follow this thread: LLM → RAG → Model evaluation. That sequence moves from the basic object or relationship to the decisions and checks it supports.
Sources and scope
Primary documentation checked on 27 September 2026. Platform features and eligibility can change; examples and cartoon situations are illustrative.
