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Prompting in Practice: Stack, Prompts and Use Cases

updated 2026-07-044 min read19 connected nodes

Pipelining LLM calls where each step's output feeds the next. This guide pulls together everything on Onexial tagged prompting — 19 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.

Core concepts behind Prompting

Before wiring anything together, the vocabulary has to be precise. These 12 definitions cover the terms that show up in almost every Prompting discussion — each one links to a full entry with an example and its own connections inside the graph.

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Prompt Chaining

Pipelining LLM calls where each step's output feeds the next.

#ai#prompting
/prompt-chainingopen →
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Chain of Thought

Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks.

#ai#prompting
/chain-of-thoughtopen →
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Few-Shot Prompting

Showing the model 2–5 examples of the task inside the prompt so it mirrors the pattern.

#ai#prompting
/few-shot-promptingopen →
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System Prompt

A high-priority instruction that sets the model's role, tone and constraints for the whole conversation.

#ai#prompting
/system-promptopen →
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Temperature

The randomness knob on an LLM's output distribution.

#ai#prompting
/temperatureopen →
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Top-p (Nucleus Sampling)

Restricting sampling to the smallest set of tokens whose probability sums to p.

#ai#prompting
/top-popen →
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JSON Mode

A model setting that guarantees valid JSON output.

#ai#prompting
/json-modeopen →
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Structured Generation

Constraining decoding to match a schema at every token.

#ai#prompting
/structured-generationopen →
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Self-Consistency

Sampling multiple answers and picking the majority to reduce errors.

#ai#prompting
/self-consistencyopen →
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Prompt Template

A reusable prompt with named variables filled at runtime.

#ai#prompting
/prompt-templateopen →
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Chain-of-Thought Prompting

Instructing a model to think step by step before answering.

#ai#prompting
/cot-promptingopen →
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Zero-Shot Prompting

Asking a model to do a task with no examples in the prompt.

#ai#prompting
/zero-shotopen →

Prompts you can reuse

Prompts are reusable components. Each of these 7 prompts is written to be dropped into a Prompting workflow with minimal editing, including the context it expects and an example output.

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