How Prompting Actually Works
Pipelining LLM calls where each step's output feeds the next. Prompt Chaining is the practice of decomposing a complex task into a sequence of smaller LLM calls, each handling one transformation, to increase reliability and debuggability. 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.
Prompt Chaining
Pipelining LLM calls where each step's output feeds the next.
Chain of Thought
Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks.
Few-Shot Prompting
Showing the model 2–5 examples of the task inside the prompt so it mirrors the pattern.
System Prompt
A high-priority instruction that sets the model's role, tone and constraints for the whole conversation.
Temperature
The randomness knob on an LLM's output distribution.
Top-p (Nucleus Sampling)
Restricting sampling to the smallest set of tokens whose probability sums to p.
JSON Mode
A model setting that guarantees valid JSON output.
Structured Generation
Constraining decoding to match a schema at every token.
Self-Consistency
Sampling multiple answers and picking the majority to reduce errors.
Prompt Template
A reusable prompt with named variables filled at runtime.
Chain-of-Thought Prompting
Instructing a model to think step by step before answering.
Zero-Shot Prompting
Asking a model to do a task with no examples in the prompt.
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.
SEO Title + Meta Description Generator
Generate 5 title/meta pairs optimized for click-through and keyword coverage.
FAQ Block Generator (AEO-Optimized)
Turn any article into 5 crisp FAQ Q&A pairs, formatted for FAQPage schema.
Personalized Cold Outbound Email
Write a 4-sentence cold email that opens with a real hook, not a fake compliment.
Changelog Entry → Launch Tweet
Turn a dry changelog line into a punchy tweet that actually gets engagement.
RAG Answer With Strict Citations
Force the LLM to answer only from provided chunks and cite them by ID.
Prompt Improver Prompt
Iterate a weak prompt into a strong one using best practices.
JSON Extractor Prompt
Extract structured JSON from any unstructured text.
Frequently asked questions
- What is Prompt Chaining?
- Prompt Chaining is the practice of decomposing a complex task into a sequence of smaller LLM calls, each handling one transformation, to increase reliability and debuggability.
- What is an example of Prompt Chaining?
- Step 1 extracts entities, step 2 classifies them, step 3 writes a summary referencing each classification.
- Why does Prompt Chaining matter for AI and automation?
- Pipelining LLM calls where each step's output feeds the next. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is Chain of Thought?
- Chain-of-Thought (CoT) is a prompting technique where the model writes intermediate reasoning steps before the final answer. Newer 'reasoning' models (o-series, Claude thinking, Gemini thinking) run CoT internally.
- What is an example of Chain of Thought?
- Instead of 'What is 17 * 24?', prompt 'Think step by step, then answer. What is 17 * 24?' — the model breaks it into partial products and gets the correct 408.
- Why does Chain of Thought matter for AI and automation?
- Prompting an LLM to reason step-by-step before answering, often improving accuracy on hard tasks. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
- What is Few-Shot Prompting?
- Few-shot prompting teaches the model by example rather than by instruction. Include input/output pairs in the prompt; the model generalises the pattern to your new input. Cheap alternative to fine-tuning for structured tasks.
- What is an example of Few-Shot Prompting?
- For product-name classification: give 3 examples ('iPhone 15 → phone', 'MacBook Air → laptop', 'AirPods → audio') then ask 'iPad Pro →'.