FLUX.2-dev-Turbo Explorer
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6x faster FLUX.2 image generation with 8-step Turbo
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efecelik
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this is a very good study. it reminded me of a time a few years ago when i found things like "few shots" and similar things ridiculous; that was a big mistake.
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From Prompt Engineering to Context Engineering: Main Design Patterns
Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.
To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:
▪️ 9 Prompt Engineering Techniques (configuring input text)
1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.
2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165
3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756
4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"
5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903
6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171
7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629
Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe
Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.
To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:
▪️ 9 Prompt Engineering Techniques (configuring input text)
1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.
2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165
3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756
4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"
5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903
6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171
7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629
Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe
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biggest gap in open source datasets is high quality, diverse data for ai, especially in scientific reasoning, multilingual, and multimodal domains
open interpreter's my fave 'cause it runs locally, no cloud, and speeds up coding from the terminal
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Open-source DeepResearch – Freeing our search agents
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