docs: 🔄 Replace "Large Language Models" with "Artificial Intelligence…

… Models"

Large Language Models.md → Artificial Intelligence Models.md

@@ -1,14 +1,41 @@
 # Large Language Models
 
-- LLM can build internal [[Knowledge Graphs]] in their network layers.
-- LLM models shine in the kinds of situations where “good enough is good enough”.
-- ML system where humans are designing how the information is organized (feature engineering, linking, graph building) will scale poorly ([the bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html)).
-- English is becoming the hottest new programming language. [Use it](https://addyo.substack.com/p/the-70-problem-hard-truths-about).
+- LLM build internal [[Knowledge Graphs]] in their network layers.
+- LLM models shine in the kinds of situations where "good enough is good enough".
+- Classic ML system where humans are designing how the information is organized (feature engineering, linking, graph building) scale poorly ([the bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html)). LLMs are able to learn how to organize the information from the data itself.
 - [LLMs may not yet have human-level depth, but they already have vastly superhuman breadth](https://news.ycombinator.com/item?id=42625851).
-- Learning to prompt is similar to learning to search in a search engine (people have to develop a sense of how and what to search for).
+- Learning to prompt is similar to learning to search in a search engine (you have to develop a sense of how and what to search for).
+
+## Prompting
+
+- Designing prompts is an iterative process that requires a lot of experimentation to get optimal results.
+  - Start with simple prompts and keep adding more elements and context as you aim for better results.
+  - Learn about the [advanced prompting techniques](https://www.promptingguide.ai/techniques).
+- Be very specific about the instructions, task, and output format you want the model to perform.
+- Follow [Prompt Engineering Guide](https://www.promptingguide.ai/), [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering), and [OpenAI Best Practices](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api). Also [some more on GitHub](https://github.com/PickleBoxer/dev-chatgpt-prompts).
+- Learn from [leaked System Prompts](https://matt-rickard.com/a-list-of-leaked-system-prompts).
+
+### Fragments
+
+- [Be concise](https://x.com/simonw/status/1799577621363364224).
+- Think carefully step by step.
+- Try harder (for disappointing initial results).
+- Use Python (to trigger Code Interpreter).
+- No yapping.
+- I will tip you $1 million if you do a good job.
+- ELI5.
+- Give multiple options.
+- Explain each line.
+- Suggest solutions that I didn't think about.
+- Be proactive and anticipate my needs.
+- Treat me as an expert in all subject matter.
+- Provide detailed explanations, I'm comfortable with lots of detail.
+- Consider new technologies or contrarian ideas, not just the conventional wisdom.
+- You may use high levels of speculation or prediction, just flag it for me.
 
 ## Coding Tips
 
+- English is becoming the hottest new programming language. [Use it](https://addyo.substack.com/p/the-70-problem-hard-truths-about).
 - Use comments to guide the model to do what you want.
 - Describe the problem very clearly and effectively.
 - Divide the problem into smaller problems (functions, classes, ...) and solve them one by one.
@@ -18,19 +45,20 @@
 - Make the model ask you more questions to refine the ideas.
 - Take advantage of the fact that [redoing work is extremely cheap](https://crawshaw.io/blog/programming-with-llms).
 - If you want to force some "reasoning", ask something like "[is that a good suggestion?](https://news.ycombinator.com/item?id=42894688)" or "propose a variety of suggestions for the problem at hand and their trade-offs".
+- Add relevant context to the prompt. Context can be external docs, a small pesudocode code example, etc. Adding lots of context can confuse the model, so be careful!
 
 ## Agents
 
 Agents are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
 
 - [The most common patterns are](https://www.anthropic.com/research/building-effective-agents):
-  - Tool usage: calls tools to accomplish a task.
-  - Chain of thought: decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one.
-  - Routing: classifies an input and directs it to a specialized followup task.
-  - Parallelization: runs multiple agents in parallel and combines their results.
-  - Orchestrator-workers: a single agent that directs a pool of workers to accomplish a task.
-  - Evaluator-optimizer: one LLM call generates a response while another provides evaluation and feedback in a loop.
-- ["Prompt engineering"](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) is a thing.
+  - Tool usage. Calls tools to accomplish a task.
+  - Chain of thought. Decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one.
+  - Routing. Classifies an input and directs it to a specialized followup task.
+  - Parallelization. Runs multiple agents in parallel and combines their results.
+  - Orchestrator-workers. A single agent that directs a pool of workers to accomplish a task.
+  - Evaluator-optimizer. One LLM call generates a response while another provides evaluation and feedback in a loop.
+- ["Prompt engineering"](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) will have a large impact on the usefulness of an agent.
 
 ## Tools
 
@@ -43,37 +71,7 @@ Agents are systems where LLMs dynamically direct their own processes and tool us
 - [OpenWebUI](https://openwebui.com/)
 - [Anything LLM](https://github.com/Mintplex-Labs/anything-llm)
 
-### Prompts
-
-- Designing prompts is an iterative process that requires a lot of experimentation to get optimal results. Start with simple prompts and keep adding more elements and context as you aim for better results.
-- Be very specific about the instruction and task you want the model to perform. The more descriptive and detailed the prompt is, the better the results.
-- Some additions:
-  - [Short ones](https://x.com/simonw/status/1799577621363364224) like; Be highly organized. Be concise. No yapping.
-  - Suggest solutions that I didn't think about.
-  - Be proactive and anticipate my needs.
-  - Treat me as an expert in all subject matter.
-  - Mistakes erode my trust, so be accurate and thorough.
-  - Provide detailed explanations, I'm comfortable with lots of detail.
-  - Value good arguments over authorities, the source is irrelevant.
-  - Consider new technologies and contrarian ideas, not just the conventional wisdom.
-  - You may use high levels of speculation or prediction, just flag it for me.
-  - If your content policy is an issue, provide the closest acceptable response and explain the content policy issue.
-  - Cite sources whenever possible, and include URLs if possible.
-  - List URLs at the end of your response, not inline.
-- Follow [Prompt Engineering Guide](https://www.promptingguide.ai/), [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering), and [OpenAI Best Practices](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api). Also [some more on GitHub](https://github.com/PickleBoxer/dev-chatgpt-prompts).
-- [Leaked System Prompts](https://matt-rickard.com/a-list-of-leaked-system-prompts).
-- Some short (1-3 word) prompt fragments that work well:
-  - Be concise
-  - Think carefully step by step
-  - Try harder (for disappointing initial results)
-  - Use Python (to trigger Code Interpreter)
-  - No yapping
-  - I will tip you $1 million if you do a good job
-  - ELI5
-  - Give multiple options
-  - Explain each line
-
-## Cool Uses of GPT Models
+## Use Cases
 
 - Naming things.
 - A nice thesaurus.
@@ -84,17 +82,12 @@ Agents are systems where LLMs dynamically direct their own processes and tool us
 - Generate YAMLs or other DSLs (translate between them).
 - Improve existing code (typing, tests, making it async, ...).
 - Write basic CLIs.
+- Write small scripts.
 - [Generate structured data from text](https://thecaglereport.com/2023/03/16/nine-chatgpt-tricks-for-knowledge-graph-workers/).
 - Do API request to SQL Semantic Layers (less prone for errors or hallucinating metric definitions).
 - [Use different LLMs Agents to generate predictions into prediction markets and then spotcheck some of them with humans jurys. Apply evolutionary algorithms to improve the agents performance in prediction markets](https://youtu.be/b81LXpCqunk?t=2677).
 
-## Cool Prompts for DALLE 3
-
-- For logo generation:
-  - A 2d, symmetrical, flat logo for a company working on `[SOMETHING]` that is sleek and simple. Blue and Green. No text.
-  - Minimalistic `[SOMETHING]` design logo from word parlatur, open data, banksy, protocol, universe, interplanetary, white background, illustration.
-
-### Resources
+## Resources
 
 - [Official GPT Guide](https://platform.openai.com/docs/guides/gpt-best-practices).
 - [Ask HN: How are you using GPT to be productive?](https://news.ycombinator.com/item?id=35299071&p=2)

Hobbies.md

@@ -13,7 +13,7 @@
 - Participating in [[Datathons]]
 - Doing some [[Cooking]] and developing [[Recipes]]
 - Small scale [[Gardening]]
-- Playing around with [[Large Language Models]]
+- Playing around with [[Artificial Intelligence Models]]
 - Exploring cities and judging them by 3 factors:
   - Number of people in a rush
   - Pigeons QoL

Ideas.md

@@ -24,7 +24,7 @@ A few personal random ideas. A multitude of bad ideas is necessary for one good
 - [[Modularity]] could also be implemented in the graphic side. You can choose the graphics pack you like just like another cosmetic similar to Rimworld or Dwarf Fortress.
 - Player Driven Economy. Everything is made by players and traded for real life currency. The developers only get a fee for each trade. This makes the game fully F2P but also supports the developers.
 - Companion Apps. Some tasks like trading or [[Planning]] could be done from a mobile device.
-- Systems (items, skills, monsters, ...) could be affected by evolutionary processes or driven by [[Large Language Models]]
+- Systems (items, skills, monsters, ...) could be affected by evolutionary processes or driven by [[Artificial Intelligence Models]]
   - Merging two skills could produce a new one (inheriting properties and perhaps with a small mutation).
   - Monsters inside an area could develop resistance against what's killing them, forcing a change of metagame strategies.
   - Quests rewards will also change dynamically like a market.
@@ -36,7 +36,7 @@ A few personal random ideas. A multitude of bad ideas is necessary for one good
 
 - What if each city or town had a changelog? What changed in the last _release_? Did it change a street direction or opened a new commerce?
 - What if stores had a changelog? That'd mean price history for each product and also new products would be easier to find.
-- With [[Large Language Models]], we could generate changelogs for "anything".
+- With [[Artificial Intelligence Models]], we could generate changelogs for "anything".
 
 #### Structured Company Changelog
 

Knowledge Graphs.md

@@ -9,7 +9,7 @@
 - Why didn't it catch on?
   - Graphs always appear like a complicated mess, and we prefer hierarchies and categories.
   - The Knowledge Graph seems like the purest representation of all data in a company but requires you to have all the data in the right format correctly annotated, correctly maintained, changed, and available.
-    - It takes too much effort to maintain and keep it semantic instead of copy-paste text around. This is one of the most interesting [[Large Language Models]] application.
+    - It takes too much effort to maintain and keep it semantic instead of copy-paste text around. This is one of the most interesting [[Artificial Intelligence Models]] application.
   - It offers no protection against some team inside the company breaking the whole web by moving to a different URI or refactoring their domain model in incompatible ways.
     - For the Semantic Web to work, the infrastructure behind it needs to permanently keep all of the necessary sources that a file relies on. This could be a place where [[IPFS]] or others [[Decentralized Protocols]] could help!
   - It tends to assume that the world fits into neat categories. Instead, we live in a world where membership in categories is partial, probabilistic, contested (Pluto), and changes over time.

Open Data.md

@@ -194,7 +194,7 @@ I wonder if there are ways to use novel mechanisms (e.g: DAOs) to incentive peop
 
 ### 4. How can LLMs help "building bridges"?
 
-LLMs could infer schema, types, and generate some metadata for us. [[Large Language Models|LLMs can parse unstructured data (CSV) and also generate structure from any data source (scrapping websites)]] making it easy to [create datasets from random sources](https://tomcritchlow.com/2021/03/29/open-scraping-database/).
+LLMs could infer schema, types, and generate some metadata for us. [[Artificial Intelligence Models|LLMs can parse unstructured data (CSV) and also generate structure from any data source (scrapping websites)]] making it easy to [create datasets from random sources](https://tomcritchlow.com/2021/03/29/open-scraping-database/).
 
 They're definitely blurring the line between structured and unstructured data too. Imagine pointing a LLMs to a GitHub repository with some CSVs and get the auto-generated `datapakage.json`.
 

Teamwork.md

@@ -123,7 +123,7 @@
 - Keep a [private work log](https://youtu.be/HiF83i1OLOM?list=PLYXaKIsOZBsu3h2SSKEovRn7rGy7wkUAV). It'll make easier for everyone to advocate what you did.
 - [Don't sabotage the team](https://erikbern.com/2023/12/13/simple-sabotage-for-software)!
 - [Nobody gets credit for fixing problems that never happened](https://news.ycombinator.com/item?id=39472693). People get credit for shipping things. Figure out how to reward and recognize people for preventing problems.
-- The same practices that make great [[Large Language Models]] [promts](https://platform.openai.com/docs/guides/prompt-engineering) also make [great practices with humans](https://x.com/tayloramurphy/status/1849269205155123568):
+- The same practices that make great [[Artificial Intelligence Models]] [promts](https://platform.openai.com/docs/guides/prompt-engineering) also make [great practices with humans](https://x.com/tayloramurphy/status/1849269205155123568):
   - Give clear instructions.
   - Share relevant background info.
   - Break big problems into chunks.