Guides

See also: Terms, Models and Applications

The Guides section of AI Wiki is a curated index of how-to articles, explainer pages, reading lists, and reference material that guide readers from a first encounter with artificial intelligence toward practical work with large language models, prompt design, retrieval pipelines, fine-tuning, and safety. It is a navigational hub, not a tutorial in itself: every linked title opens a dedicated wiki page covering that topic in depth, and the sections are ordered to mirror a learning path from foundational vocabulary through prompting, building, training, and alignment. The hub is written for self-directed learners, software engineers, researchers, students, and writers, and it is rebuilt from the wiki's PostgreSQL database as new guide-style content lands. As of 2026-05-09, AI Wiki contained more than 2,000 entries, and this gateway is updated whenever new guide-style content is added.

How do I use this page?

This is a top-level index, not a tutorial in itself. Each linked title takes the reader to a dedicated wiki page covering that topic in depth. The order of sections roughly mirrors a learning path: start with foundational vocabulary and concepts, move to prompt design, then build practical applications with LLMs, then study how models are trained and aligned, and finally consult curated reading lists, podcasts, talks, and conference summaries.

Readers who only want a quick reference can jump to the Acronyms and Abbreviations pages, both of which list common short forms used across AI research and industry writing. Readers who want a structured curriculum can follow the path described in the "Suggested learning paths" table further down the page.

Getting started with AI

New readers usually need three things first: a working vocabulary, a sense of what current models can and cannot do, and a basic mental model of how a transformer processes text. The articles in this section are written to give that foundation without assuming a research background. The transformer architecture that underpins every model below was introduced in the 2017 paper Attention Is All You Need, which proposed an architecture "based solely on attention mechanisms, dispensing with recurrence and convolutions entirely."^[2]

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A reader new to the field can finish Artificial intelligence, Large Language Model, and Transformer in a single sitting and come away with enough vocabulary to follow most other pages on the wiki. The Tokenization and Embeddings entries are useful next reads because almost every later topic, from prompt design to retrieval, depends on understanding how text becomes numbers.

What is prompt engineering?

Prompt engineering is the practice of writing inputs to language models so the output matches what the user wants. The wiki covers the core techniques in long-form pages and supplements them with shorter rule-of-thumb articles.

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Readers who want to start with one article should pick Prompt Engineering. It introduces every term used in the more specialised pages. The 26 Principles of Good Prompts page is a good follow-up because it converts the survey into 26 concrete instructions that can be applied immediately.^[1] After that, Chain-of-Thought Prompting and ReAct (prompting) cover the two patterns that most agentic systems rely on. Chain-of-thought prompting was shown to raise GSM8K math-word-problem accuracy for a 540B-parameter model from 17.9% with standard prompting to 56.9% with chain-of-thought exemplars.^[8]

How do I build applications with LLMs?

Once the basics are in place, the next step is wiring a language model into an application. The wiki covers the API mechanics, the agent patterns, and the protocols that have appeared between 2023 and 2026.

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For a developer building a first prototype, a sensible reading order is: GPT API, then Function calling, then Tool use (artificial intelligence), then Retrieval-augmented generation. The Model Context Protocol page covers the open standard introduced by Anthropic in November 2024 to standardize how AI systems connect to external tools and data sources; Anthropic released it with SDKs for Python, TypeScript, C#, and Java, and donated the protocol to the Linux Foundation's Agentic AI Foundation in December 2025.^[11] The Vector database page lists the storage systems most often used to back retrieval pipelines, including pgvector, Pinecone, Weaviate, Milvus, Qdrant, and Chroma.

How are models fine-tuned and trained?

Fine-tuning takes a pre-trained model and adapts it to a narrower task or style. The wiki covers both classical full-parameter fine-tuning and the parameter-efficient methods that have become standard since 2021.

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A reader who wants to fine-tune an open-weight model on a single GPU should start with LoRA (Low-Rank Adaptation) and QLoRA.^[3][4] Both pages walk through the rank parameter, the alpha parameter, and the trade-offs between LoRA, QLoRA, and full fine-tuning. The original LoRA paper reported that the method can reduce the number of trainable parameters by up to 10,000 times and the GPU memory requirement by 3 times relative to full fine-tuning of GPT-3 175B.^[3] The Quantization page is a useful companion because QLoRA is built on 4-bit NF4 quantization; QLoRA enabled fine-tuning of a 65B-parameter model on a single 48GB GPU while preserving full 16-bit fine-tuning performance.^[4] For preference training, the wiki covers both RLHF, the original approach used in InstructGPT and ChatGPT, and DPO, the simpler alternative published by Rafailov and colleagues in 2023.^[5][6]

What is AI safety and alignment?

Safety and alignment are covered as separate but linked topics. Safety articles describe the risks and mitigations; alignment articles describe the technical problem of getting a model to pursue the goals its developers actually intend.

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The AI safety and AI Alignment pages are the two most important entry points. They link out to specific topics like reward hacking, deceptive alignment, evaluations, red-teaming, and interpretability. The two short "how to" pages cover concrete defensive measures: one for model owners worried about extraction, and one for website operators who do not want their content scraped for training data.

ChatGPT-specific guides

See also: ChatGPT Guides

This section collects pages aimed specifically at users of ChatGPT and the underlying GPT API.

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Reading lists

The wiki maintains separate index pages for books, podcasts, blog posts, videos, and films. These are linked below with a short note on what each one contains.

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The Books entry has been the most stable of these lists. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville is still the most widely cited textbook on neural networks.^[12] Superintelligence by Nick Bostrom is the standard reference for long-term safety arguments.^[13] The Podcasts entry favours episodes that pair a serious technical guest with a long-form interviewer, so the conversations stay substantive across an hour or more.

Conferences and talks

The wiki indexes the major academic conferences and a small number of industry events. Conference pages cover history, paper acceptance rates, notable papers, and venue information. Talk summary pages cover individual sessions in detail.

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The NeurIPS page is the best starting point for a reader new to academic machine learning research. It explains the review process, lists notable past papers, and gives a sense of what kinds of work get accepted. The Dartmouth Conference page is more historical: the term "artificial intelligence" was coined in the proposal dated August 31, 1955 by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, and the 1956 workshop it called for is generally treated as the founding event of the field.^[15]

The two OpenAI Dev Day 2023 talk pages are kept because both sessions remain useful even years later. A Survey of Techniques for Maximizing LLM Performance covers a four-quadrant model that puts prompt engineering, retrieval, and fine-tuning on the same axes, and it is still widely cited as a quick way to think about which technique fits which problem.

Suggested learning paths

The table below groups guides into learning paths. Readers do not have to follow them in order, but each row works as a self-contained sequence.

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What are the longest guides on the wiki?

The table below shows the longest guide-style explainer pages currently on the wiki, grouped by the topic they cover. Word counts are taken from the database as of 2026-05-09.

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Ranking the pages this way is useful for two reasons. First, the longest articles tend to be the most thoroughly cross-linked, so they double as hubs into smaller related pages. Second, when several articles in a topic appear in the table, that topic is well covered in the wiki and a reader can rely on it for serious study.

How is this gateway maintained?

This page is rebuilt whenever new guide-style articles are added to the wiki, when existing articles cross significant length or quality thresholds, or when a new technique becomes prominent enough to deserve its own row in the tables. The lists are derived from queries against the wiki's PostgreSQL database. Word counts and titles are pulled directly from the pages table, so they reflect what is actually published rather than what was planned.

If an article is missing from this index but exists on the wiki, that usually means one of three things: the article does not yet have the Guides category, the article is shorter than the cut-off used in the table, or the article belongs to a more specialised gateway page such as Models, Applications, or Terms. Readers who notice gaps can check those gateways before assuming a topic is uncovered.

Related gateway pages

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References

1. Bsharat, S. M., Myrzakhan, A., and Shen, Z. (2024). *Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4*. arXiv:2312.16171. Source for the 26 Principles of Good Prompts page. ↩
2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017). *Attention Is All You Need*. NeurIPS 2017. Foundation paper for the Transformer and Attention Is All You Need pages. ↩
3. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). *LoRA: Low-Rank Adaptation of Large Language Models*. arXiv:2106.09685. Source for the LoRA (Low-Rank Adaptation) page. ↩
4. Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). *QLoRA: Efficient Finetuning of Quantized LLMs*. arXiv:2305.14314, NeurIPS 2023. Source for the QLoRA page. ↩
5. Ouyang, L., Wu, J., Jiang, X., et al. (2022). *Training language models to follow instructions with human feedback*. NeurIPS 2022. Foundational paper for the RLHF page. ↩
6. Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C. (2023). *Direct Preference Optimization: Your Language Model is Secretly a Reward Model*. NeurIPS 2023. Source for the DPO page. ↩
7. Lewis, P., Perez, E., Piktus, A., et al. (2020). *Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks*. NeurIPS 2020. Source for the Retrieval-augmented generation page.
8. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., and Zhou, D. (2022). *Chain-of-Thought Prompting Elicits Reasoning in Large Language Models*. NeurIPS 2022. Source for the Chain-of-Thought Prompting page. ↩
9. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K. (2023). *Tree of Thoughts: Deliberate Problem Solving with Large Language Models*. NeurIPS 2023. Source for the Tree of Thoughts page.
10. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. (2023). *ReAct: Synergizing Reasoning and Acting in Language Models*. ICLR 2023. Source for the ReAct (prompting) page.
11. Anthropic (2024). *Introducing the Model Context Protocol*. Anthropic news release, November 25, 2024. Source for the Model Context Protocol page. ↩
12. Goodfellow, I., Bengio, Y., and Courville, A. (2016). *Deep Learning*. MIT Press. Listed on the Books page. ↩
13. Bostrom, N. (2014). *Superintelligence: Paths, Dangers, Strategies*. Oxford University Press. Listed on the Books page. ↩
14. Kurzweil, R. (2005). *The Singularity Is Near: When Humans Transcend Biology*. Viking Press. Listed on the Books page.
15. McCarthy, J., Minsky, M., Rochester, N., and Shannon, C. (1955). *A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence*, dated August 31, 1955. Source for the Dartmouth Conference page. ↩