@cassie: Making spooky party favors out of APPLE CIDER ๐Ÿคฏ

Cassie
Cassie
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Region: US
Thursday 22 September 2022 23:16:15 GMT
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cassie
Cassie :
I promise Iโ€™m fun at parties ๐Ÿ˜‚
2022-09-22 23:21:29
4
jocelynsummers587
Jocelyn :
Hot apple cider would be so good right now
2022-09-22 23:24:28
4
sarahtrips
Sarah trips :
And now make pumpkin spice latte please ๐Ÿฅฐ
2022-09-23 17:49:51
3
s_m.c0
S๐ŸŽ€ :
First ๐Ÿฅฐ
2022-09-22 23:19:36
2
amie9532
๐€๐Œ๐ˆ๐„๊จ„ :
@cocolodge
2022-09-24 20:54:06
2
jazspher.u
jazspher :
this early 6th comment
2022-09-23 00:08:52
1
milliefamily8
Millie :
Hi looks so good ๐Ÿ˜Š
2022-09-23 12:50:46
1
hxille
hxille :
wom
2022-09-26 17:41:25
1
_.aliyahx
aliyah๐ŸŽ€ :
@sabsrina_33
2022-10-29 08:42:06
1
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LLMs are no longer just fancy autocomplete engines. Weโ€™re seeing a clear shiftโ€”from single-shot prompting to techniques that mimic ๐—ฎ๐—ด๐—ฒ๐—ป๐—ฐ๐˜†: reasoning, retrieving, taking action, and even coordinating across steps. In this visual, Iโ€™ve laid out five core prompting strategies: - ๐—ฅ๐—”๐—š โ€“ Brings in external knowledge, enhancing factual accuracyย ย  - ๐—ฅ๐—ฒ๐—”๐—ฐ๐˜ โ€“ Enables reasoning ๐—ฎ๐—ป๐—ฑ acting, the essence of agentic behaviorย ย  - ๐——๐—ฆ๐—ฃ โ€“ Adds directional hints through policy modelsย ย  - ๐—ง๐—ผ๐—ง (๐—ง๐—ฟ๐—ฒ๐—ฒ-๐—ผ๐—ณ-๐—ง๐—ต๐—ผ๐˜‚๐—ด๐—ต๐˜) โ€“ Simulates branching reasoning paths, like a mini debate inside the LLMย ย  - ๐—–๐—ผ๐—ง (๐—–๐—ต๐—ฎ๐—ถ๐—ป-๐—ผ๐—ณ-๐—ง๐—ต๐—ผ๐˜‚๐—ด๐—ต๐˜) โ€“ Breaks down complex thinking into step-by-step logic While not all of these are fully agentic on their own, techniques like ๐—ฅ๐—ฒ๐—”๐—ฐ๐˜ and ๐—ง๐—ผ๐—ง are clear stepping stones to ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ โ€” where autonomous agents can ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ผ๐—ป, ๐—ฝ๐—น๐—ฎ๐—ป, ๐—ฎ๐—ป๐—ฑ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฎ๐—ฐ๐˜ ๐˜„๐—ถ๐˜๐—ต ๐—ฒ๐—ป๐˜ƒ๐—ถ๐—ฟ๐—ผ๐—ป๐—บ๐—ฒ๐—ป๐˜๐˜€. The big picture?ย  Weโ€™re slowly moving from
LLMs are no longer just fancy autocomplete engines. Weโ€™re seeing a clear shiftโ€”from single-shot prompting to techniques that mimic ๐—ฎ๐—ด๐—ฒ๐—ป๐—ฐ๐˜†: reasoning, retrieving, taking action, and even coordinating across steps. In this visual, Iโ€™ve laid out five core prompting strategies: - ๐—ฅ๐—”๐—š โ€“ Brings in external knowledge, enhancing factual accuracyย ย  - ๐—ฅ๐—ฒ๐—”๐—ฐ๐˜ โ€“ Enables reasoning ๐—ฎ๐—ป๐—ฑ acting, the essence of agentic behaviorย ย  - ๐——๐—ฆ๐—ฃ โ€“ Adds directional hints through policy modelsย ย  - ๐—ง๐—ผ๐—ง (๐—ง๐—ฟ๐—ฒ๐—ฒ-๐—ผ๐—ณ-๐—ง๐—ต๐—ผ๐˜‚๐—ด๐—ต๐˜) โ€“ Simulates branching reasoning paths, like a mini debate inside the LLMย ย  - ๐—–๐—ผ๐—ง (๐—–๐—ต๐—ฎ๐—ถ๐—ป-๐—ผ๐—ณ-๐—ง๐—ต๐—ผ๐˜‚๐—ด๐—ต๐˜) โ€“ Breaks down complex thinking into step-by-step logic While not all of these are fully agentic on their own, techniques like ๐—ฅ๐—ฒ๐—”๐—ฐ๐˜ and ๐—ง๐—ผ๐—ง are clear stepping stones to ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ โ€” where autonomous agents can ๐—ฟ๐—ฒ๐—ฎ๐˜€๐—ผ๐—ป, ๐—ฝ๐—น๐—ฎ๐—ป, ๐—ฎ๐—ป๐—ฑ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฎ๐—ฐ๐˜ ๐˜„๐—ถ๐˜๐—ต ๐—ฒ๐—ป๐˜ƒ๐—ถ๐—ฟ๐—ผ๐—ป๐—บ๐—ฒ๐—ป๐˜๐˜€. The big picture?ย  Weโ€™re slowly moving from "๐˜ฑ๐˜ณ๐˜ฐ๐˜ฎ๐˜ฑ๐˜ต ๐˜ฆ๐˜ฏ๐˜จ๐˜ช๐˜ฏ๐˜ฆ๐˜ฆ๐˜ณ๐˜ช๐˜ฏ๐˜จ" to "๐˜ค๐˜ฐ๐˜จ๐˜ฏ๐˜ช๐˜ต๐˜ช๐˜ท๐˜ฆ ๐˜ข๐˜ณ๐˜ค๐˜ฉ๐˜ช๐˜ต๐˜ฆ๐˜ค๐˜ต๐˜ถ๐˜ณ๐˜ฆ ๐˜ฅ๐˜ฆ๐˜ด๐˜ช๐˜จ๐˜ฏ." And thatโ€™s where the real innovation lies. #promptengineering #AI #artificialintelligence #AIAGENT #softwareengineering #AgenticAl

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