Large Language Models (LLM): Complete Guide in 2026

Everything you need to know about LLM

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Introduction

If you are building, fine-tuning, evaluating, or procuring data for a large language model in 2026, this guide is your complete reference. The LLM landscape has undergone rapid change: frontier models now operate as multimodal agents, alignment techniques have evolved from basic RLHF to direct preference optimization (DPO), and regulators in the EU are beginning to enforce training data documentation requirements.

This guide cuts through the noise. It explains what LLMs are and how they work, maps the four stages of the LLM training data pipeline, provides a scored vendor evaluation framework, and gives you the decision criteria to choose between building, fine-tuning, or using retrieval-augmented generation (RAG) for your use case.

Who is this Guide for?

This guide is written for:

AI product leaders and heads of AI deciding on LLM strategy and vendor selection
ML engineers and research scientists defining data requirements for training or fine-tuning
Data procurement and sourcing teams evaluating training data service providers
Legal and compliance teams assessing data provenance, licensing risk, and regulatory obligations
Founders and startup CTOs building LLM-powered products and choosing between model strategies

LLM vs. Generative AI vs. Multimodal AI vs. Agentic AI

Term	Definition	Examples
Large Language Model (LLM)	A text-focused transformer model trained on massive text corpora via self-supervised learning.	Llama 3, Mistral, GPT-4 (text-only)
Generative AI (GenAI)	Broad category of AI systems that generate content (text, image, audio, video, code).	ChatGPT, Midjourney, Suno, Sora
Multimodal AI	AI models that process and generate across multiple modalities (text + image, text + audio, etc.).	GPT-4V, Gemini 1.5, LLaVA, Claude 3
Agentic AI	AI systems that autonomously execute multi-step tasks using tools, APIs, and external memory.	AutoGPT, Claude Computer Use, Devin
Foundation Model	A large pretrained model used as a base for downstream fine-tuning or prompt-based deployment.	Most frontier LLMs serve as foundation models

LLM Glossary

LLM stands for Large Language Model. Additional terms buyers encounter:

SFT (Supervised Fine-Tuning): Training a base model on curated instruction-response pairs with explicit labels
RLHF (Reinforcement Learning from Human Feedback): Alignment method using human preference rankings to train a reward model and then optimize the LLM via RL
RLAIF (Reinforcement Learning from AI Feedback): Variant where an AI model generates preference labels instead of, or in addition to, human annotators
DPO (Direct Preference Optimization): Alignment method that optimizes directly on preference pairs without a separate reward model — simpler and increasingly preferred over PPO-based RLHF
RAG (Retrieval-Augmented Generation): Architecture that supplements LLM generation with real-time retrieval from an external knowledge base
Token: The basic unit of text an LLM processes; roughly 0.75 words in English
Context window: The maximum number of tokens an LLM can process in a single inference call

The LLM Training Process: Step by Step

Before diving into each stage in detail, here is the end-to-end process in plain language — covering the steps that directly affect training data decisions:

Gather and curate source data: Collect raw text from diverse sources — web crawls, books, code repositories, academic papers, and domain-specific corpora. The goal is broad coverage of human language. At scale, this means hundreds of billions to trillions of tokens. Curation is non-negotiable: remove duplicates, filter low-quality content, strip PII, and apply toxicity classifiers before any model ever sees the data.
Preprocess and tokenize: The raw text is cleaned, normalized, and broken into tokens — the basic units the model processes. Tokens are typically sub-word units (using algorithms like BPE or SentencePiece), meaning a single word may become 1–3 tokens. The tokenized corpus is then serialized into the format the training infrastructure expects.
Pretrain the base model: The model is trained on the full preprocessed corpus using self-supervised learning — predicting the next token from context, over and over, across trillions of examples. The model adjusts its hundreds of billions of parameters to reduce prediction error. This stage requires massive compute (thousands of GPUs running for weeks to months) and produces a base model that has broad language understanding but no specific behavior or alignment.
Run supervised fine-tuning (SFT): The base model is trained on a curated set of (instruction, ideal response) pairs written or verified by skilled human annotators. This stage is where the model learns to follow instructions, adopt the right tone, and apply domain knowledge. Data quality at this stage is the primary determinant of downstream product quality.
Apply preference alignment (RLHF or DPO): Human raters evaluate multiple model responses for the same prompt and rank them. These rankings are used to align the model toward outputs that are helpful, safe, and honest. This stage is what converts an instruction-following model into a production-grade assistant. Inter-annotator agreement (IAA) and rater calibration are the critical quality metrics to track.
Evaluate and red-team: The fine-tuned, aligned model is systematically evaluated on benchmark test sets and subjected to adversarial red-teaming to find safety failures, hallucination patterns, and bias issues. Findings feed back into the training data pipeline — identified failure modes become new training examples in the next SFT or alignment iteration.
Iterate via the data flywheel: After deployment, real user interactions (where permitted and consented) surface new failure modes, edge cases, and domain gaps. These are reviewed, annotated, and fed back into the training pipeline in regular cycles. The teams that improve fastest are those with the shortest loop between deployed model failures and new training data.

LLM Training Data Types by Stage: Reference Table

Training Stage	Data Type	Typical Format	Scale	Human Involvement	Key Quality Criteria
Pretraining	Web text, books, code, papers, multilingual corpora	Plain text / tokenized	100B–15T tokens	Minimal (quality filtering only)	Deduplication, PII removal, language quality, toxicity filtering
SFT (Fine-Tuning)	Instruction-response pairs	JSON: {prompt, completion}	10K–1M examples	High (expert writers/reviewers)	Response accuracy, format compliance, tone, factual grounding
RLHF / DPO (Alignment)	Human preference rankings	JSON: {prompt, chosen, rejected}	50K–500K pairs	High (trained preference raters)	IAA scores, demographic diversity, rater calibration, safety coverage
RLAIF	AI-generated preference labels + human validation	JSON: {prompt, chosen, rejected, ai_label}	100K–10M+ pairs	Medium (human validation sample)	AI judge calibration, false positive rate on safety labels
Evaluation / Benchmarks	Test prompts with gold-standard answers	JSON/CSV: {prompt, reference_answer}	1K–100K items	High (expert annotators)	Coverage of failure modes, no leakage from training data
Red-Teaming	Adversarial prompts targeting safety, bias, jailbreaks	JSON: {prompt, failure_category, severity}	500–50K prompts	High (specialized red-teamers)	Failure mode coverage, prompt diversity, safety taxonomy alignment
Multimodal SFT	Image-text pairs, visual instruction data	JSON + image files: {image, prompt, response}	10K–1M pairs	High (annotators + validators)	Caption accuracy, visual grounding, OCR quality
Agentic / Tool-Use	Multi-turn reasoning traces, tool-call logs	JSON: {trace, actions, observations, outcome}	1K–100K traces	High (domain experts)	Trace correctness, tool-call accuracy, failure mode coverage

How Much Training Data Does an LLM Need? (2026 Reference)

One of the most common questions buyers ask is: how much data do I actually need? The answer depends on which stage of the training pipeline you are in. The industry measures data volume in tokens — not gigabytes — because token count is what the model actually processes, regardless of raw file size.

As a reference point: one trillion tokens is approximately 750 billion words, or roughly equivalent to millions of books. Modern frontier models like Llama 3 (405B) and Gemini 1.5 were trained on datasets in the 10-15 trillion token range. However, for fine-tuning and alignment — the stages most buyers are actually procuring data for — the volumes are far more manageable.

Training Stage	Data Volume (Tokens / Examples)	Rough File Size Equivalent	Who Typically Procures This	Key Constraint
Pretraining (from scratch)	100B - 15T+ tokens	~80 GB - 12 TB of text	Frontier model labs (Google, Meta, Anthropic, Mistral)	Compute cost, deduplication, legal clearance
Domain-Adaptive Pretraining	1B - 100B tokens	~800 MB - 80 GB	Enterprises training domain-specific base models	Domain coverage, data licensing
Supervised Fine-Tuning (SFT)	10K - 1M examples	~10 MB - 2 GB (JSON)	Any org fine-tuning an open-weight model	Annotation quality, domain expert access
Preference Alignment (RLHF/DPO)	50K - 500K preference pairs	~50 MB - 500 MB (JSON)	Orgs building production-grade assistants	Rater calibration, IAA scores, safety coverage
RLAIF (AI-labeled preference)	100K - 10M+ pairs	~100 MB - 10 GB	Orgs scaling alignment on open-weight models	AI judge calibration, human validation sample rate
Evaluation / Benchmarks	1K - 100K test items	~1 MB - 100 MB	All fine-tuning projects	No leakage from training data; expert annotation
Red-Teaming Suite	500 - 50K adversarial prompts	~0.5 MB - 50 MB	All production-facing deployments	Failure mode coverage, taxonomy alignment
Multimodal SFT (image+text)	10K - 1M image-text pairs	10 GB - 1 TB (with images)	Orgs building vision-language products	Image quality, annotation accuracy, visual grounding

What this means for your data procurement budget: The three stages where most enterprise buyers are actually procuring data — SFT, preference alignment, and evaluation — represent a small fraction of pretraining scale. A well-curated SFT dataset of 50,000-200,000 high-quality examples consistently outperforms raw datasets 10-50x larger with poor annotation quality. Invest in quality control and annotator expertise before scaling volume.

Converting tokens to GB: As a rough rule, 1 GB of plain English text contains approximately 800 million to 1 billion tokens depending on the tokenizer and content type. Code is denser per byte (more tokens per KB). Multilingual corpora vary significantly by language and script.

Popular LLM Examples in 2026

The LLM landscape in 2026 is characterized by a mix of proprietary frontier models and open-weight alternatives that organizations can fine-tune on their own data.

Model	Organization	Type	Notable Characteristics
GPT-4 / GPT-4o	OpenAI	Proprietary, multimodal	Dominant in enterprise; strong coding, reasoning, vision
Claude 3 / Claude 3.5	Anthropic	Proprietary	Strong on safety, long context (200K tokens), nuanced instruction following
Gemini 1.5 Pro / Ultra	Google DeepMind	Proprietary, multimodal	1M token context window; strong on multimodal and code
Llama 3 (8B, 70B, 405B)	Meta	Open-weight	Most widely fine-tuned open model; strong performance per parameter
Mistral / Mixtral 8x22B	Mistral AI	Open-weight, MoE	Efficient mixture-of-experts; strong European privacy credentials
Phi-3 (3.8B, 14B)	Microsoft	Open-weight	Strong performance at small scale; suited for edge deployment
Qwen 2	Alibaba	Open-weight	Strong multilingual coverage including Chinese, Arabic, and 26 other languages
Command R+	Cohere	Proprietary	Optimized for enterprise RAG and grounded generation

LLM Use Cases by Industry in 2026

Understanding relevant use cases helps define the training data requirements before engaging a vendor.

Build vs. Buy vs. Fine-Tune vs. RAG: Decision Framework

Before procuring training data, clarify which model strategy applies to your situation. Each path has different data requirements and cost profiles.

Strategy	When to Choose	Data Requirements	Estimated Effort	Key Risk
Use API (no training)	General tasks, fast time-to-market, limited budget	None (prompt engineering only)	Low	Data privacy, vendor lock-in, limited customization
RAG (retrieval-augmented)	Tasks requiring current or proprietary knowledge	Clean, chunked knowledge base docs	Medium	Retrieval quality, hallucination on edge cases
SFT Fine-Tuning	Domain-specific tone, format, or knowledge; consistent behavior	10K–500K instruction-response pairs	High	Catastrophic forgetting, data quality bottlenecks
Full RLHF/DPO Alignment	Safety-critical, public-facing, or regulated applications	SFT data + 50K–500K preference pairs + red-team suite	Very High	Annotator cost, reward hacking, alignment tax
Train from Scratch	Unique domain (highly specialized language/code), IP ownership	1T+ tokens of domain-specific text	Extremely High	Resource cost, technical risk, long timeline

Synthetic Data: Benefits, Risks, and Best Practices

Synthetic data — generated by an LLM or other model — can accelerate data collection and fill coverage gaps in rare domains. However, buyers should approach it with clear-eyed expectations.

Benefits: Rapid scaling for low-resource domains, privacy-preserving (no PII), cost-efficient for initial pipeline development, and useful for augmenting edge cases.

Risks: Model collapse — models trained predominantly on synthetic data from the same model family can degrade in output diversity and factual accuracy over iterations. Hallucinations from the generating model can propagate as ground truth into the trainee model. Evaluation benchmarks must remain grounded in real human-authored gold sets to avoid circular contamination.

Best practice: Treat synthetic data as a draft or starting point. Always validate a representative sample with human expert review before including in production training runs. Aim for a human-verified, real-data core (typically 30–60% of SFT and 100% of evaluation/red-team datasets).

Data Provenance, Licensing, and Copyright Risk in 2026

Data provenance — knowing where your training data came from, who owns it, and under what conditions it was collected — has moved from a ‘nice to have’ to a legal obligation in regulated markets.

Key developments driving urgency:

Ongoing copyright litigation in the US (including The New York Times v. OpenAI) has established that scraped web content carries meaningful legal risk for commercial model development.
The EU AI Act, effective August 2026 for general-purpose AI, requires providers of frontier models to document training data sources and demonstrate compliance with copyright law.
Growing enterprise demand for 'clean room' training datasets from legally cleared, consent-based sources for regulated industry deployments

What to ask your data vendor:

Do you have data subject consent documentation for personally generated content?
Which data sources were used? Is the provenance documented per item or per batch?
What is your copyright clearance process for web-sourced text?
Does your data governance SLA include indemnification for copyright claims?
Are you compliant with GDPR Article 17 (right to erasure) for training data subjects?

Multimodal LLMs: Training Data for Vision, Audio, and Video

Multimodal models process and generate across text, images, audio, and video. Building or fine-tuning multimodal LLMs requires specialized data types beyond the text pipeline.

Modality Combination	Data Type	Annotation Task	Key Quality Metric
Image + Text	Image-caption pairs, visual QA, OCR	Caption writing, bounding box annotation, text transcription	Caption accuracy, visual grounding precision
Audio + Text	Speech transcripts, audio descriptions, multilingual speech	Transcription, speaker diarization, sentiment labels	WER (word error rate), speaker accuracy
Video + Text	Video captions, action labels, temporal QA	Segment annotation, action recognition, QA pairs	Temporal alignment accuracy, captioning quality
Document (PDF/scan) + Text	Document parsing, table extraction, layout understanding	Structure annotation, entity extraction	Field extraction accuracy, layout F1 score
Code + Natural Language	Code with comments, docstrings, NL-to-code pairs	Code review, docstring writing, correctness checking	Functional correctness (pass@k), NL alignment

LLM Red-Teaming and Safety Evaluation

Red-teaming is the systematic adversarial testing of an LLM to identify failure modes before deployment. It covers safety (harmful content generation), reliability (hallucination, inconsistency), security (prompt injection, jailbreaks), and bias (discriminatory outputs across demographic groups).

A structured red-team engagement typically includes:

Defining the threat model: What harms are most likely given the deployment context?
Building a prompt taxonomy: Organize adversarial prompts by failure category, severity, and affected population
Automated probing: Use automated tools to generate and score thousands of adversarial variants
Human red-teaming: Deploy specialized human red-teamers for high-severity or nuanced failure modes that automation misses
Reporting and remediation: Document findings per taxonomy category and feed findings back into the SFT/alignment data pipeline

Regulatory context: The EU AI Act (Article 55) requires providers of general-purpose AI models with systemic risk to conduct adversarial testing. NIST AI RMF and ISO 42001 also reference red-teaming as part of AI risk management. Even organizations not subject to EU law are increasingly required by enterprise customers to provide red-team assessment documentation.

How to Evaluate and Select an LLM Training Data Vendor

Most vendors promise the same things: “high quality,” “fast delivery,” and “expert annotators.” The real differences show up later—when rejection rates rise and timelines slip.

To spot a strong vendor early, ask specific, process-level questions. If they can explain how they work (not just what they offer), that’s a good sign. If they dodge details, that’s a warning.

1. Data Quality: How do you ensure quality before delivery?

What steps happen between annotation and final delivery?
Who reviews the work, and how often?
Do you use multi-pass QA and a separate QA team?
If a batch fails QA, who pays and how fast is rework?

2. Annotator Expertise: Who will work on my project?

Are annotators domain experts, generalists, or a mix?
How do you train and calibrate raters before production?
Is your rater pool diverse enough for global deployment?

3. Pipeline Coverage: Can you support everything I need?

Do you support SFT, RLHF/DPO, eval sets, multilingual, multimodal?
Can you share samples: dataset, guidelines, and a relevant customer reference?
Are languages covered by native speakers (not machine translation)?

4. Data Provenance: Where does the data come from?

What contributor consent do you collect (and does it cover AI training)?
Can you support deletion requests (right to erasure)?
What’s your retention and deletion policy after delivery?

5. Security and Compliance: What do you have today?

Do you have SOC 2 Type II? Can you share proof?
ISO 27001 certified—what scope?
Can you sign HIPAA (if needed)?
Do you provide GDPR DPA, and where does EU data stay?
How do you isolate client data to prevent cross-client exposure?

6. Capacity and Timeline: What can you deliver realistically?

How many qualified annotators are available right now?
How long to ramp up and deliver the first QA-reviewed batch?
Can you scale volume quickly? What’s your surge capacity?
What usually causes delays, and how do you prevent them?

7. Pricing: What’s the true all-in cost?

Does pricing include QA, rework, and project management?
What if guidelines change mid-project and work must be redone?
Any minimum commitment or penalties if scope changes?

8. Pilot: Will you prove quality before full scale?

Will you run a paid pilot (200–500 items) on the real task?
If it fails, do you redo it at no extra cost?
Will the pilot team stay on for production?

9. References: Who can I speak to?

Can you share 2–3 relevant customer references?
Do you have case studies with measurable outcomes?
Tell me about a project that went wrong—and how you fixed it.

10. Partnership: How do you work after first delivery?

Do we get a dedicated PM/QA lead, or will the team rotate?
What’s turnaround time for follow-on batches?
How do you investigate systematic errors found later?
How do you retrain teams when guidelines change?

How to Run an LLM Data Pilot / POC

A structured pilot de-risks vendor selection and surfaces quality issues before full contract commitment.

Define a representative sample: Choose 200–500 items that cover the edge cases and domain complexity of your full dataset.
Provide a detailed annotation guide with examples: Your quality bar is only as high as the clarity of your guidelines.
Set acceptance criteria in writing before the pilot starts: Specify minimum score, error rate, and turnaround time.
Hold a mid-pilot calibration call: Review disagreements and ambiguous cases with the vendor’s QA team.
Audit the pilot output independently: Have 1–2 domain experts on your team review a random 10% sample blind.
Request a vendor’s own QA report: Ask what defects they caught and corrected before delivery.
Evaluate turnaround time vs. quoted SLA: Pilot speed often predicts production speed.

Market Outlook: LLMs and AI Training Data in 2026

The LLM market is entering a phase of consolidation and vertical specialization. After the rapid proliferation of foundation model releases in 2023–2024, organizations are now focused on making LLMs work reliably in production — which places higher demands on fine-tuning data quality, evaluation rigor, and governance infrastructure.

Key trends shaping the training data market in 2026:

Increasing demand for preference and alignment data: As more organizations fine-tune open-weight models (Llama, Mistral, Phi), the bottleneck has shifted from compute to high-quality RLHF/DPO preference data
Multimodal data growth: Vision-language models are now standard in enterprise deployments, driving demand for image-text annotation at scale
Agentic AI data as an emerging category: Multi-step reasoning traces and tool-use supervision data are nascent but growing rapidly as agent deployments scale
Regulatory-driven provenance requirements: EU AI Act compliance documentation requirements are creating demand for auditable, consent-based data pipelines
Synthetic + human hybrid pipelines: Pure human annotation is too slow for the iteration speeds demanded by modern AI development; the market is moving toward synthetic generation with human validation loops

Common Mistakes When Training or Procuring LLM Data

Starting without a written annotation guide: Annotators cannot maintain consistency without explicit examples of edge cases. Always invest in a detailed annotation guide before production begins.

Optimizing for quantity over quality: More data with lower quality typically degrades model performance beyond a threshold. Curated, high-quality SFT datasets of 50K–100K items routinely outperform raw datasets of 10M+ items.

Skipping the pilot: Full-volume contracts with unvetted vendors routinely discover quality issues that could have been caught in a 500-item pilot costing a fraction of the full project.

Treating synthetic data as equivalent to human data: Synthetic data is a supplement, not a replacement. Models trained on synthetic-only preference data have shown alignment degradation in independent evaluations.

Neglecting evaluation data: Many teams invest heavily in training data and underinvest in evaluation. A robust eval suite (including adversarial red-team cases) is necessary to measure whether your training investment is working.

Ignoring data provenance: In regulated industries or public-facing deployments, an inability to document data sources can block product launch or create retroactive legal liability.

Using the same dataset for training and evaluation: Benchmark contamination is a documented problem. Maintain strict train/eval separation and prefer held-out evaluation sets that were never in the vendor's training pipeline.

Why Shaip Is the Right LLM Training Data Partner for Your Project

Throughout this guide, we have outlined what it takes to build, fine-tune, and evaluate large language models: the right data at each training stage, rigorous quality control, provenance documentation, domain expertise, and a vendor capable of supporting you from initial pilot through production scale. This section maps those requirements directly to what Shaip provides — based entirely on verified services, not claims.

Full-Pipeline Coverage Across All Four LLM Training Stages

Most training data vendors specialize in one or two stages of the pipeline. A common limitation is vendors who handle annotation well but have no red-teaming capability, or marketplaces with broad reach but no domain expert annotators for specialized tasks.

Shaip is structured to support the complete LLM training pipeline from a single partner:

LLM Training Stage	What Buyers Need	Shaip Service
Pretraining Data Curation	High-quality, diverse, filtered text corpora; multilingual coverage; PII removal	Data Collection (text, audio, images, video) + Data Licensing (off-the-shelf curated datasets)
Supervised Fine-Tuning (SFT)	Expert-written instruction-response pairs; domain-specific annotation; prompt and response generation	Fine-Tuning Solutions + AI Prompt and Response Generation
Preference Alignment (RLHF / DPO)	Human preference rankings; trained rater pools; IAA-tracked annotation; prompt-chosen-rejected triplets	RLHF Solutions
Retrieval-Augmented Generation (RAG)	Clean, structured knowledge base documents; chunked and tagged for retrieval accuracy	RAG Solutions
Multimodal Training Data	Image-text pairs, audio-text pairs, visual instruction tuning, OCR data, video annotation	Multimodal AI Solutions
Evaluation and Red-Teaming	Adversarial prompt suites; safety and bias testing; failure mode documentation	Red Teaming Services
Conversational AI and Speech	Multilingual transcription, speaker diarization, dialogue datasets in 65+ languages	Conversational AI + Speech Data Catalog (65+ languages)
Healthcare and Medical LLMs	HIPAA-compliant annotation; clinical expert reviewers; de-identified medical datasets	Healthcare AI Solutions + Medical Data Catalog

Next Steps

Every LLM project is different in scope, domain, and stage. Whether you are running your first fine-tuning experiment on an open-weight model, building a production RLHF pipeline, or preparing for a multimodal deployment, the starting point is the same: define your data requirements clearly before you talk to anyone.

If you are ready to discuss your LLM training data requirements with Shaip, visit shaip.com/contact-us/ or explore specific service pages for Fine-Tuning, RLHF, Multimodal AI, RAG, and Conversational AI at shaip.com/solutions/generative-ai.

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Frequently Asked Questions (FAQ)

1. Relationship of AI, ML, Deep Learning, LLM and Generative AI

DL is a subfield of ML that utilizes artificial neural networks with multiple layers to learn complex patterns in data. ML is a subset of AI that focuses on algorithms and models that enable machines to learn from data. Large language models (LLMs) are a subset of deep learning and share common ground with generative AI, as both are components of the broader field of deep learning.

2. What are Large language models?

Large language models, or LLMs, are expansive and versatile language models that are initially pre-trained on extensive text data to grasp the fundamental aspects of language. They are then fine-tuned for specific applications or tasks, allowing them to be adapted and optimized for particular purposes.

3. Benefits of using Large language models

Firstly, large language models possess the capability to handle a wide range of tasks due to their extensive training with massive amounts of data and billions of parameters.

Secondly, these models exhibit adaptability as they can be fine-tuned with minimal specific field training data.

Lastly, the performance of LLMs shows continuous improvement when additional data and parameters are incorporated, enhancing their effectiveness over time.

4. Prompt Design Vs Prompt Engineering

Prompt design involves creating a prompt tailored to the specific task, such as specifying the desired output language in a translation task. Prompt engineering, on the other hand, focuses on optimizing performance by incorporating domain knowledge, providing output examples, or using effective keywords. Prompt design is a general concept, while prompt engineering is a specialized approach. While prompt design is essential for all systems, prompt engineering becomes crucial for systems requiring high accuracy or performance.

5. Kinds of large language models

There are three types of large language models. Each type requires a different approach to promoting.

Generic language models predict the next word based on the language in the training data.
Instruction tuned models are trained to predict response to the instructions given in the input.
Dialogue tuned models are trained to have a dialogue-like conversation by generating the next response.