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- Chain-of-Thought Prompting 2026: Techniques, Examples and Pitfalls
- Few-Shot vs. Zero-Shot Prompting: Which Technique When in 2026?
- Structured Outputs with AI 2026: JSON, XML and Reliable Parsing
- System Prompts & Role Prompting 2026: The Practitioner's Guide
- AI Agents 2026: Claude Computer Use, OpenAI Operator and ChatGPT Atlas Compared
Update history (2)
- Reasoning models (OpenAI o1/o3, Claude Thinking) added, multimodal prompts introduced as a dedicated chapter, the five-phase workflow brought up to date.
- Original publication with seven core techniques, five-phase workflow and a decision matrix for prompt engineering vs. RAG vs. fine-tuning.
Prompt engineering used to carry an aura of craft knowledge passed between practitioners like a guild secret. In 2026 that aura has faded. What remains is something more useful and more testable: a small, well-defined set of techniques, a decision framework for when to apply each, and a growing professional practice around evaluation, versioning and team-wide standards. The models became stronger, the guardrails tighter and the tooling mature — which means the bar for “good prompting” is now higher, not lower.
This guide condenses the current state of practice. It walks through the seven techniques that account for almost every serious prompting task, compares them honestly on cost and complexity, shows where classic chain-of-thought has become redundant because reasoning models like OpenAI o1 and o3 now think internally, and explains how to combine prompting with retrieval and fine-tuning when a plain prompt is no longer the right tool.
Whether you write prompts for ChatGPT, Claude or Gemini, the underlying patterns are the same. Model-specific quirks matter at the margins — XML for Claude, Markdown for GPT, multimodal for Gemini — but the structural decisions carry across every major system.
Short answer
Prompt engineering in 2026: what reasoning models actually changed
The biggest shift since 2024 is not a new technique but a new model class. OpenAI’s o1 and o3, Claude 3.5 Sonnet Thinking and Gemini 2.0 Thinking perform an internal reasoning phase before they produce a visible answer. They generate an extended chain of hidden tokens in which they plan, check assumptions, revise and self-correct. For the user this means two things: the answer quality on hard logical, mathematical and legal tasks jumps noticeably, and explicit instructions like “think step by step” often add nothing because the model is already doing exactly that — just out of sight.
The practical consequence is that the shape of a good prompt changes depending on which model you send it to. For a standard model like GPT-4.5 or Claude 3.5 Sonnet Base, an explicit reasoning scaffold still helps. For a reasoning model, the same scaffold is redundant at best and sometimes even harmful, because a rigid step plan can constrain the internal reasoning the model would otherwise perform more fluidly. Cost is the second variable: reasoning models are five to ten times more expensive per request and noticeably slower. A well-structured prompt on a standard model often beats a lazy prompt on a reasoning model on both quality and budget.
What has not changed is everything downstream of the raw reasoning: clear task definition, explicit context, output schema, role assignment and honest examples. These are the structural tools that carry any prompt, reasoning model or not. The rest of this guide takes them one by one.
The 7 prompt engineering techniques that cover 95% of real-world tasks
Over the past two years the community has consolidated on a short list. If you can apply these seven techniques fluently, you cover almost every prompt you will ever write professionally. They are Zero-Shot, Few-Shot, Chain-of-Thought, Role Prompting, Self-Consistency, ReAct and Structured Output. Everything else — think CoVe, Tree-of-Thought, Skeleton-of-Thought or PAL — is a specialisation worth reaching for only once the basics fail.
The shortest way to keep them straight is to think about what each technique adds to a prompt. Zero-Shot adds nothing; it is the default. Few-Shot adds examples. Chain-of-Thought adds reasoning steps. Role Prompting adds persona. Self-Consistency adds repetition and voting. ReAct adds external tools. Structured Output adds a schema. Each added layer raises both quality and cost.
| Technique | Complexity | Cost overhead | Typical use case | Model dependency |
|---|---|---|---|---|
| Zero-Shot | low | none | everyday tasks, summaries, rewrites | works on every model |
| Few-Shot | low | +20–40 % tokens | style transfer, format calibration | strong on all frontier models |
| Chain-of-Thought | medium | +30–80 % tokens | math, logic, legal, planning | redundant on o1/o3/Thinking |
| Role Prompting | low | minimal | domain voice, expert perspective | universal |
| Self-Consistency | high | 3–10× cost | high-stakes single answers | best on reasoning models |
| ReAct | high | variable | agents with tool use, retrieval | needs tool-call support |
| Structured Output | low | minimal | pipelines, automation, APIs | native on GPT-4.5, Claude 3.5, Gemini 2.0 |
The table is a rough map, not a law. In practice you often stack them: a role-prompted, few-shot, structured-output prompt is common in production pipelines. You rarely stack all seven at once — each layer adds latency, tokens and places where something can go wrong.
A pragmatic default: start with Zero-Shot plus Structured Output. Add Few-Shot when format drifts. Add Role Prompting when voice matters. Reach for Chain-of-Thought or a reasoning model when logic fails. Reserve Self-Consistency and ReAct for the cases where reliability and tool access justify the extra machinery.
Zero-shot vs few-shot prompting: when does each still pay off in 2026?
Zero-shot prompting is simply asking the model to perform a task without supplying examples. Two years ago this was fragile; today, on frontier models, it handles the majority of knowledge-work tasks out of the box. Writing a summary, rewriting a paragraph in a different tone, extracting named entities from a short text, translating a memo, drafting a first outline — a clean zero-shot prompt handles all of these reliably.
You are writing for a B2B audience of technical product managers.
Summarise the following release notes in 120–150 words.
Keep three sections: "What changed", "Why it matters", "What to do next".
No marketing language; factual, sober tone.
[release notes here]The limit of zero-shot shows up in three situations. First, whenever the output format is non-obvious: a specific JSON shape, a particular table layout, an unusual citation style. Second, whenever the task depends on tacit domain knowledge the model has to mimic rather than merely understand — legal drafting style, a particular brand voice, the house conventions of a scientific journal. Third, whenever the task is novel enough that the model’s prior is wrong; edge cases in a business rule, for instance, or a translation that must respect in-house terminology.
In those cases few-shot prompting pays for itself immediately. Two to five carefully chosen examples often lift output quality by 20–40 % and, more importantly, make the output consistent enough to parse downstream. The examples do three jobs at once: they pin the format, they calibrate the tone and they show the model how to handle edge cases you cannot easily describe in prose.
Classify customer emails into ["billing", "technical", "feedback", "spam"].
Return only the label.
Email: "Your invoice for March is wrong, I was charged twice."
Label: billing
Email: "The login button on mobile doesn't respond after the update."
Label: technical
Email: "Great product, love the new dashboard."
Label: feedback
Email: "[new email to classify]"
Label:A few practical rules save hours of iteration. Examples should cover the hardest cases you expect, not the easiest. Order matters: put the most similar example closest to the task. Avoid examples that leak the answer to the specific input you are about to ask about; that produces great eval numbers and useless generalisation. For production systems, consider dynamic few-shot: a small vector index retrieves the most relevant examples per input at runtime — this is the 2026 default for any classifier or extractor on more than a handful of categories.
For a full decision tree with cost estimates, the companion piece Few-Shot vs Zero-Shot prompting: when to use which technique works through twelve concrete scenarios side by side.
Chain-of-thought and reasoning models o1/o3: where classic CoT becomes obsolete
Chain-of-thought prompting is the technique of asking the model to reason out loud before producing a final answer. On pre-2025 models the gain was dramatic — on GSM8K-style math word problems, CoT lifted accuracy from the low 20s to the high 50s. On frontier non-reasoning models the gain is smaller but still real: perhaps +20 to +35 % on genuinely multi-step tasks.
A customer ordered 3 items at €49, applied a 15 % voucher,
paid €20 shipping and returned one item. The refund policy
keeps 10 % of the returned item's price as a restocking fee.
Compute the final balance owed to the customer.
Reason step by step. Show each calculation. Only at the end,
output the balance as a single number.What changed with reasoning models is that the step-by-step reasoning now happens inside the model as hidden “thinking tokens” before any visible output. On OpenAI o1 and o3, on Claude 3.5 Sonnet Thinking and on Gemini 2.0 Thinking, explicit “think step by step” instructions no longer add meaningful accuracy — the model is already doing more thorough planning than any user-written scaffold. On these models, explicit CoT sometimes hurts, because a fixed plan can over-constrain the internal exploration.
The practical rule in 2026 is therefore simple. On a non-reasoning model, reach for CoT whenever the task has real multi-step logic: arithmetic, legal argument, causal analysis, planning, scheduling. On a reasoning model, write the task cleanly and let the model think in its own way — you are paying for that extra internal computation, so do not fight it. And for either family, if your output needs to be machine-parseable, ask the model to reason first and then produce a final, structured answer as a last step; never try to parse the middle.
A practical variant is structured CoT, where you give the model a rough thinking template:
Task: [clearly stated task]
Step 1 — Restate what is asked in your own words.
Step 2 — List all constraints and known facts.
Step 3 — Propose two candidate approaches; pick one with reasoning.
Step 4 — Execute the chosen approach.
Step 5 — Sanity-check the result against the constraints.
Final answer: [concise, in the requested format]This works well on non-reasoning models and gracefully degrades on reasoning models: they still respect the final-answer format but do most of the heavy lifting internally. A deeper walkthrough with benchmarks is in Chain-of-Thought prompting: techniques and examples.
Self-consistency is a close cousin worth mentioning briefly: instead of running one CoT prompt, run the same prompt five to ten times with higher temperature and take the majority answer. It is expensive but extremely reliable for high-stakes single-answer tasks where you cannot afford a wrong output. Reasoning models largely absorb this gain internally, which is another reason their per-call cost is higher.
System prompts and role prompting: persistent persona for consistent output
A system prompt sits outside the user turn. It is the long-lived instruction block that tells the model who it is, what it is for and which rules it must not break. A role prompt, narrower in scope, assigns a persona for a single task: “You are a senior tax advisor in Germany”. Both techniques lean on the same mechanism — models pull more heavily on training data that matches the described expertise, voice and norms.
A well-crafted system prompt is the quietest, most under-rated efficiency gain in prompt engineering. It removes the need to repeat constraints on every turn, enforces a consistent voice across a whole session and catches a surprising number of compliance and tone issues at the top of the stack.
You are the on-call prompt assistant for an internal support team.
Persona: calm, precise, no marketing language, never speculate
about policy you cannot verify from the knowledge base.
Format: short paragraphs, bullet lists only when items have
equal weight, tables only for comparisons.
Boundaries:
- Never offer legal, tax or medical advice; redirect to the
appropriate team.
- If the knowledge base does not contain the answer, say so
clearly and suggest who to ask.
- Always cite the source article slug in square brackets.
Language: mirror the user's language (English or German).The principles behind a production-quality system prompt are consistent across vendors: state the role concretely, enumerate the format rules, list the hard boundaries, and specify a fallback behaviour for out-of-scope requests. Leave out everything you cannot measure. A system prompt that says “be helpful and harmless” without concrete rules adds nothing the model did not already do.
Role prompting at the task level is simpler and often enough. “You are an experienced patent attorney specialising in software patents in Germany and the EU” yields measurably more precise legal answers than a neutral question — not because the model suddenly knows more, but because it draws on a narrower, more relevant slice of its training distribution. The more specific the role, the better the anchoring: “senior copywriter for B2B SaaS with ten years of direct-response experience” outperforms “marketer” by a wide margin.
A detailed playbook with ten annotated system-prompt templates lives in System prompts and role prompting best practices.
Structured outputs in JSON and XML: native modes in GPT-4.5, Claude 3.5, Gemini 2.0
For any output that will be parsed by downstream code, structured output is no longer optional. Every major frontier model now supports a native structured-output mode: OpenAI’s Structured Outputs with JSON Schema on GPT-4.5 and o-series, Anthropic’s XML-tag convention and JSON tool-use on Claude 3.5, Google’s controlled generation on Gemini 2.0. These modes do not merely request a format — they constrain decoding so the output is guaranteed to parse.
The shift from “ask nicely for JSON” to “declare a schema” is one of the clearer productivity wins of the year. A prompt that used to need defensive parsing, retries and regex repair now returns clean objects on the first try.
// OpenAI Structured Outputs example (JSON Schema)
{
"type": "object",
"properties": {
"summary": { "type": "string", "maxLength": 400 },
"sentiment": { "type": "string", "enum": ["positive", "neutral", "negative"] },
"action_items": {
"type": "array",
"items": { "type": "string" },
"maxItems": 5
}
},
"required": ["summary", "sentiment", "action_items"],
"additionalProperties": false
}For Claude the idiomatic equivalent is XML tags inside the prompt:
<task>Review the following support transcript and extract actions.</task>
<transcript>
[transcript text]
</transcript>
<output_format>
<summary>one paragraph, under 400 characters</summary>
<sentiment>positive | neutral | negative</sentiment>
<action_items>
<item>short imperative sentence</item>
... up to 5 items
</action_items>
</output_format>Two practical lessons are worth internalising. First, keep the schema as tight as you can: every optional field and every free-text string is a place for drift. Enums and bounded arrays beat open strings whenever the domain allows. Second, put the structural specification at the end of the prompt, after context and examples — models attend more strongly to the last instructions they see, and structural rules are the ones you least want to see violated.
For multilingual pipelines, always add an explicit language field rather than guessing from content. For long documents, structure the output as chunks with explicit IDs; reconstructing order from free-text output is a recipe for off-by-one bugs. The full pattern library with ready-to-ship schemas is collected in Structured outputs in JSON and XML.
Multimodal prompts: combining image, text and audio
Multimodal prompting was a novelty in 2024 and a production reality in 2026. GPT-4.5 with Vision, Claude 3.5 Sonnet with image input and Gemini 2.0 — which remains the most natural at mixing modalities — all accept images, and the leading models now accept audio as well, either as raw waveform or as transcript plus timing.
The core trick with multimodal prompts is that the same clarity rules still apply: role, task, context, examples, format. The only addition is being explicit about which modality carries which information. Vague prompts on images degrade faster than vague prompts on text, because the model has to decide both what you meant and what it is looking at.
You are reviewing a product screenshot for accessibility issues.
Inputs:
- [IMAGE] a screenshot of a sign-up form
- [TEXT] the WCAG 2.2 criteria we enforce internally
Task:
1. Identify every UI element visible in the screenshot.
2. For each element, check it against the listed criteria.
3. Flag any violation with: element name, criterion, severity.
Return the result as a JSON array following the schema below.For document-heavy workflows, combining a PDF (as image pages or extracted text) with a structured prompt outperforms either modality alone. For voice notes and meetings, audio-in plus a transcription-aware prompt reduces hallucinated names and numbers dramatically, because the model can disambiguate from pronunciation as well as context.
Two warnings. Multimodal output is still mostly text or structured data; “generate an image in response” is a separate product surface that needs its own design. And multimodal prompts are noticeably more expensive per request — price your pipeline assuming 3–10× the token cost of a pure-text equivalent.
The 10 most common prompt mistakes and how to avoid them
The failure modes of prompting are remarkably stable across users, teams and models. If you can recognise these ten patterns, you will debug most bad outputs in minutes instead of hours.
The first is task stacking — packing multiple independent tasks into one prompt. The model handles the first, dilutes the second, and often drops the third entirely. The fix is either explicit numbering with output headers or, better, prompt chaining: one prompt per task, piped together.
The second is absent output format. If you do not say “return a Markdown table” or “reply as JSON”, the model picks. It usually picks inconsistently. Always specify the shape you want, even when the answer is a single sentence.
The third is missing context. A prompt that says “rewrite this email” with no information about audience, goal or register produces a polite but generic rewrite. Two or three sentences of context typically eliminate five rounds of iteration.
The fourth is negative instructions as the main lever. “Do not use marketing language” is weaker than “use a sober, factual tone with no adjectives of degree”. Models respond more reliably to affirmative descriptions of what you want than to bans on what you do not want.
The fifth is overloaded role prompts. “You are a world-class expert polymath futurist strategist” collapses into generic output. One specific role, with two or three concrete anchors, outperforms stacked superlatives every time.
The sixth is stale examples. Few-shot examples left over from an earlier version of the task silently cap quality. Re-review your examples every time you change the task specification.
The seventh is invisible constraints. The user knows the deadline, the budget, the audience, the regulatory frame — and never writes it down. Put the constraints in the prompt; the model cannot infer a German KWG requirement from the phrasing of a question.
The eighth is format drift across turns. In a multi-turn conversation, the output format erodes after four or five turns as the model drifts toward conversational prose. A short “remember to reply in the original JSON schema” at the start of the turn fixes it immediately.
The ninth is treating the model as a search engine. Asking for a list of sources or current facts without retrieval will produce plausible, wrong citations. If you need current or authoritative information, attach it or run the prompt behind a retrieval system.
The tenth is no evaluation. Prompts are edited for months without anyone checking whether each change helped or hurt. A twenty-example eval set, run before and after every significant prompt change, is the single biggest quality lever most teams do not pull.
A prompt-engineering workflow: from draft to production-ready template
A repeatable workflow turns prompt writing from a guessing game into a short, predictable process. The version below works equally well for a solo knowledge worker and for a five-person applied-AI team.
Stage one: the rough draft. Write the prompt as you would describe the task to a new junior colleague. Include the role, the goal, the context, the task itself, any constraints, the output format and — if helpful — one example. Do not over-polish; the draft exists to be measured, not admired.
Stage two: the honest test set. Before touching the prompt again, collect ten to twenty real inputs from the domain, with the ideal output written down by a human who knows the work. This is the single hardest and most valuable step. A test set that reflects real data catches bugs a hundred clever re-readings miss.
Stage three: the first run. Run the draft against the test set. Read every output. Do not grade, yet — just notice the patterns. Where does the model miss? Is it format? Tone? A systematically wrong inference?
Stage four: targeted revision. Fix one failure class at a time. If format drifts, tighten the output spec. If tone is off, adjust the role prompt and add a single strong example. If reasoning is wrong, reach for CoT or a reasoning model. Resist the urge to rewrite everything in one pass; one lever per iteration makes the effect of each change visible.
Stage five: comparative evaluation. Run the revised prompt against the same test set. Either use LLM-as-judge scoring (a stronger model grades the outputs against the reference) or human grading on a simple 1–5 rubric. Only promote a new prompt if it beats the previous one on the set.
Stage six: hardening for production. Add the boring but critical scaffolding: a system prompt with boundaries, a structured-output schema, retry logic with a lowered-temperature fallback, input validation to reject malformed or malicious inputs, and a logging hook so you can replay failures later. Pin the model version explicitly; silent model upgrades are a well-known source of regression.
Stage seven: versioning and documentation. Treat prompts like code. Store them in a repository, version them, write short change notes, keep the eval set alongside. When something breaks in production three months later, the history is the fastest way to diagnose the regression.
The discipline looks heavy on paper and adds perhaps thirty minutes the first time. After three iterations it becomes reflexive and saves hours every week.
Prompt engineering vs RAG vs fine-tuning: which approach fits which problem?
The three big levers for adapting a language model to your domain are prompt engineering, retrieval augmentation (RAG) and fine-tuning. They are not competitors; they stack. The question is almost never which one but which one first.
Prompt engineering is the cheapest and fastest lever. You change behaviour by changing instructions. It is the right first move for any new use case, it adapts instantly when you change your mind, and it costs nothing beyond tokens. It fails when the required knowledge is not in the model’s training data — current facts, proprietary documents, fast-moving domains.
RAG addresses exactly that failure. Instead of hoping the model knows your content, you fetch the relevant chunks from a vector index at runtime and inject them into the prompt. RAG is the right tool whenever the knowledge is large, changing or private. Customer support over a product knowledge base, legal assistants over a firm’s case archive, internal policy bots — all RAG jobs. It fails when the task needs deep stylistic consistency or implicit reasoning that cannot be captured by retrieved passages.
Fine-tuning adjusts the model itself. It is the right tool when you have a hundred or more consistent examples, when the task is stable enough that those examples will not go stale in a month, and when the volume is high enough that the per-request token savings justify the training cost. Fine-tuned models excel at style, format and niche classification. They are poorly suited to tasks where the answer depends on changing facts — for those, RAG on a general model wins.
A practical decision path: always start with prompt engineering. If the prompt grows above roughly two thousand tokens because you keep pasting background information, move that information into a RAG system. If you still cannot get consistent behaviour despite good prompts and good retrieval, and you have the volume and stable examples, then fine-tune — and combine the fine-tuned model with the same retrieval and prompt discipline you would use on a general model. Fine-tuning replaces nothing; it sharpens what is already working.
Which prompt technique fits which task in 2026? Our concrete recommendation
Prompt engineering in 2026 is a compact, teachable discipline. Seven techniques cover almost every real task. Reasoning models have absorbed some of the burden that used to fall on elaborate chain-of-thought scaffolds, but the structural work — clear roles, concrete context, explicit formats, honest examples, measurable evaluation — has become more important, not less. The teams that prompt well are not the ones with clever tricks. They are the ones with small eval sets, versioned templates, tight output schemas and the patience to fix one failure class at a time. Everything else follows from those habits.
Sources and further reading
Technique recommendations rest on the vendors’ primary documentation: the OpenAI Cookbook documents prompt patterns, reasoning-model behaviour and structured-outputs APIs, the Anthropic prompt engineering documentation describes XML tags, chain-of-thought and Claude-specific best practices, and the Google Gemini prompting guide explains multimodal prompt strategies. For academic depth we recommend the Prompt Engineering Guide (DAIR.AI) and the arXiv cs.CL section for current research papers.
For deeper dives into each technique within concrete tool workflows, see Chain-of-Thought Prompting 2026 — Techniques and Examples, Few-Shot vs. Zero-Shot Prompting: When to use which technique, Structured Outputs in JSON/XML with Prompting 2026, System Prompts and Role Prompting – Best Practices 2026 and the ChatGPT vs. Claude vs. Gemini comparison.
Update note (as of 15.04.2026)
This guide is continuously reconciled with the model and API moves of the three leading vendors. Particular attention goes to the expected GPT-5 launch with extended reasoning, the Claude Opus 4 roll-out, the transition from Gemini 2.0 to 2.5, and new structured-output schema variants. The last refresh (15.04.2026) integrated reasoning models (o1, o3, Claude Thinking), added multimodal prompts as a dedicated chapter and rebuilt the five-phase workflow. Market-relevant interim events appear first as cluster updates on the hub.
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Frequently Asked Questions
What is Prompt Engineering in simple terms?
Prompt Engineering is the art of formulating instructions to AI models so they deliver precise, reproducible and high-quality answers. It covers structure, context, examples and iterative refinement — no programming needed.
Which prompt techniques matter most in 2026?
The five core techniques are: Chain-of-Thought (CoT), Few-Shot Prompting, Role Prompts, Structured Outputs (JSON/XML) and Prompt Chaining. They cover 90 % of all professional use cases — everything else is polish.
Do I need training in Prompt Engineering?
For simple applications, 2 hours of focused reading is enough. For enterprise use with compliance concerns, structured training pays off — errors become expensive (wrong data, production hallucinations, legal risks).
Do prompts differ between ChatGPT, Claude and Gemini?
Fundamentally no — all respond to the same techniques. Claude however responds more strongly to XML tags for structuring, ChatGPT to Markdown, Gemini to multimodal prompts with images. For critical tasks: test on all three.
What's the difference between Zero-Shot and Few-Shot prompting?
Zero-Shot: you only provide a task description, no examples. Works for 70 % of cases with modern models. Few-Shot: you add 2–5 worked examples — increases quality for structured outputs, niche jargon and style adaptation by 20–40 %.
What is Chain-of-Thought (CoT) and when do I use it?
CoT means asking the model to think through intermediate steps out loud before answering ('Think step by step'). +35 % accuracy for math, logic, legal analysis. Unnecessary for factual queries and creative text.
Is Prompt Engineering a future job?
Yes, but less as a standalone career than a baseline skill. Like Google search 20 years ago, prompt literacy will be the standard skill in all knowledge work by 2026–2030.
What are reasoning models like OpenAI o1, o3 and Claude Thinking?
Models that perform longer internal reasoning before answering. They are 5–10× more expensive than standard models but deliver better results on complex reasoning. Classic Chain-of-Thought often becomes unnecessary with these models.
How do I measure the quality of my prompts?
Three tools: (1) Eval sets with 20–50 test inputs and expected outputs. (2) LLM-as-Judge: a stronger model rates your outputs. (3) Human-in-the-loop: sample by domain experts. For production prompts: use all three in parallel.
When should I use Fine-Tuning instead of Prompt Engineering?
With 100+ consistent examples and high-volume use (>10k queries/month) fine-tuning usually pays off: more consistent outputs, lower input token costs. Below that: Few-Shot prompts with RAG (Retrieval Augmented Generation) is the more pragmatic path.
What's the most common beginner mistake when prompting?
Mixing multiple tasks in one prompt. Better: split each task into its own request or use prompt chaining. Second most common: no explicit output format — always specify structure (list, table, JSON).
Can I automate prompts?
Yes — Dynamic Few-Shot with Vector Search (RAG) is the 2026 production standard: the most fitting examples are pulled from a database at runtime. Tools like LangChain, LlamaIndex and Haystack make integration straightforward.
