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the items traced by reading the 3-part series.

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research.md

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20 20 #### Andrej Karpathy is a demi-god.
21 21 - https://x.com/karpathy/status/1748043513156272416
22 22
23 + #### More App example experiences.
23 24 - https://doordash.engineering/2020/08/28/overcome-the-cold-start-problem-in-menu-item-tagging/
24 25 description of DoorDash App.
25 26 - https://arxiv.org/pdf/2212.10481 Execution-Based Evaluation for Open-Domain Code Generation

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1 + ### The original Three articles.
2 +
3 + - https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/
4 + - https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/
5 + - https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-iii-strategy/
6 +
7 + ### And then the Rabbit Holes.
8 +
9 + - https://www.microsoft.com/en-us/research/blog/the-power-of-prompting/ Microsoft's ideas on prompt engr rather than fine-tunning.
10 + - https://www.linkedin.com/blog/engineering/generative-ai/musings-on-building-a-generative-ai-product LinkedIn's App work description.
11 + - https://jxnl.github.io/blog/writing/2024/02/28/levels-of-complexity-rag-applications/ Some ideas on whata RAG app might look like.
12 +
13 + Hamel Husain's ideas... very useful
14 + - https://hamel.dev/blog/posts/evals/
15 + - https://hamel.dev/blog/posts/prompt/
16 + - https://gist.github.com/hamelsmu/d0d75bf702e56987f35cb715f7da4d6a
17 + - https://hamel.dev/blog/posts/evals/#step-1-write-scoped-tests
18 + Write the Tests!!
19 +
20 + #### Andrej Karpathy is a demi-god.
21 + - https://x.com/karpathy/status/1748043513156272416
22 +
23 + - https://doordash.engineering/2020/08/28/overcome-the-cold-start-problem-in-menu-item-tagging/
24 + description of DoorDash App.
25 + - https://arxiv.org/pdf/2212.10481 Execution-Based Evaluation for Open-Domain Code Generation
26 + - https://www.godaddy.com/resources/news/llm-from-the-trenches-10-lessons-learned-operationalizing-models-at-godaddy#h-1-sometimes-one-prompt-isn-t-enough
27 + GoDaddy'ss LLM App Description.
28 + - https://promptengineering.org/what-are-large-language-model-llm-agents/
29 + what's a LLM-agent and why?
30 + - https://github.com/daveshap/SparsePrimingRepresentations
31 + a very clever way to condense knowledge to be used within prompts.
32 +
33 +
34 + #### Prompt frameworks
35 + - https://github.com/outlines-dev/outlines
36 + - https://github.com/jxnl/instructor
37 +
38 + #### SQL PaLM
39 + - https://www.researchgate.net/publication/371223615_SQL-PaLM_Improved_Large_Language_ModelAdaptation_for_Text-to-SQL
40 +
41 + #### Create a LLM from Scratch
42 + - https://pbs.twimg.com/card_img/1799150371614437376/EcIpp2ov?format=jpg&name=small
43 +
44 + #### Code-aid tool
45 + - https://austinhenley.com/blog/codeaid.html
46 + - https://austinhenley.com/pubs/Kazemitabaar2024CHI_CodeAid.pdf
47 +
48 + - https://lancedb.com
49 + a vector database for use to handle embedding and chunked text(?)
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