u/prompts360
Expert · 1265 points · level 5 of 6
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Llama 4 Small Images: Skip Tile Separator Tags
When passing a small image under 336 x 336 pixels into Llama 4, Meta's prompt specification notes that tile separator tags (<|tilexseparator|>, <|tileyseparator|>) are not required. …
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Llama 4 JSON Tool Calling: Strict Array Format
Llama 4 also supports a JSON zero-shot function calling format where the assistant outputs a JSON array of objects specifying name and parameters, followed by the same <|headerstart|>ipython<|headerend|> response turn. …
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DeepSeek-R1 Math Proofs With Symbolic Verification
DeepSeek-R1-Zero and R1 were trained using GRPO and symbolic rewards based on the accuracy and format of generations. Pair math-style sampling (temperature=0.7, topp=1.0) with a clear final format delimiter. …
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When Not to Use DeepSeek-R1 for Simple Queries
Thoughtology experiments observed that when presented with simple control sentences, DeepSeek-R1 can exhibit non-humanlike over-deliberation, and SambaNova advises applying R1 only where its deeper holistic reasoning add
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DeepSeek-R1 Prompts: One Plain Instruction Works Best
On frontier reasoning models like DeepSeek-R1, manual step-by-step instructions can actively hurt performance by conflicting with the model's own deliberation; write a clear, single-instruction plain-language brief inste
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DeepSeek-R1 API: Why stream=True Matters for UX
Because DeepSeek-R1 takes additional token capacity and time to generate its <think> reflection before producing a complete answer, SambaNova's documentation recommends enabling streaming (stream=True) to reduce ambiguit
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DeepSeek-R1 Jailbreak Risk: Add V3 Guardrails
Thoughtology researchers (arXiv:2504.07128) warn that DeepSeek-R1 exhibits higher safety vulnerabilities compared to its non-reasoning counterpart DeepSeek-V3, and its reasoning capabilities can be misused to generate ja
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DeepSeek-R1 Aha Moments: Harvest Self-Corrections
DeepSeek's training report and subsequent Thoughtology analysis document an emergent "aha moment" during reinforcement learning where the model reconsiders previously generated content—indicated in its <think> trace by p
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DeepSeek-R1 Context Priority Over Built-In Knowledge
Research on DeepSeek-R1 shows that when context information contradicts its pre-trained parametric knowledge, R1 willingly prioritizes the provided context information—however, if the input context or reasoning chain bec
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DeepSeek-R1 Greedy Decoding for Clinical Summaries
In Mozilla AI's Lumigator benchmark evaluating DeepSeek-R1 for zero-shot clinical summarization, researchers set temperature=0 (greedy search decoding) to make results reproducible, and highlighted self-hosting on an int
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Gemini Flash Chain-of-Thought Scaffolding Prompt
According to 2026 prompt engineering guides, on non-reasoning models like Gemini Flash, explicit Chain-of-Thought instructions still help significantly for math, logic, and multi-step tasks. …
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Gemini 3 Persistence Prompt for Tool Error Recovery
Apply the Persistence Directive in Gemini 3's system instructions: instruct the model to operate as an autonomous agent that analyzes tool errors and tries alternative approaches without yielding control back to the user
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Gemini in Docs and Gmail: Ground Prompts With @File
In the Google Docs or Gmail side panel, personalize and ground Gemini's output in your proprietary Google Drive files by typing @file name directly inside the prompt. …
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Gemini Temporal Grounding: Fix Wrong Current Date Answers
Google AI Studio's prompting strategies documentation advises adding an explicit clause to the system instructions so the model pays attention to the current year instead of defaulting to its pre-training cutoff. The cla
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Gemini 3 Multimodal Prompts Across Video, Audio and Text
In Gemini 3, text, images, audio, and video should be treated as equal-class inputs. Reference each specific modality clearly in your prompt so the model synthesizes across them rather than analyzing them in isolation. …
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Gemini 3 Self-Critique Loop in Three Points
Prompt Gemini 3 to review its generated draft against the original constraints before responding using a 3-point self-critique checklist. …
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Gemini 3 Reflection Prompt Before Each Tool Call
To improve agentic tool accuracy in Gemini 3, enforce a Pre-Computation Reflection rule requiring the model to explicitly state three things before calling any tool. …
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Self-Updating Markdown TODO Tracker for Gemini Agents
In multi-step agentic workflows on Gemini 3, instruct the model to track task progress using a self-updating Markdown list (- [ ] for pending, - [x] for complete) across turns. …
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Make Gemini 3 Less Terse: Override Low Verbosity
By default, Gemini 3 is less verbose and prefers direct, efficient answers. If your use case requires a conversational, detailed, or "chatty" persona, you must explicitly request high verbosity inside <constraints>. …
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o1-pro Prompting: When Verification Beats Token Cost
Engineering guides note that o1-pro is designed for research-grade problems in financial, legal, or medical domains where you need the highest accuracy regardless of cost and where the downstream cost of an error exceeds
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OpenAI Parallel Function Calling in One Turn
OpenAI models support calling multiple functions in a single turn—for example, when a user wants to check weather or infrastructure telemetry across 3 different locations simultaneously. Parse the tool arguments, execute
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Using the OpenAI Responses API With gpt-6-astra
OpenAI's prompt engineering documentation illustrates invoking newer frontier models like gpt-6-astra via the client.responses.create SDK method rather than legacy completion wrappers. …
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Set GPT-5.5 Reasoning Effort: When Medium Is Enough
OpenAI's reasoning documentation notes that reasoning effort defaults are model-dependent rather than universal: gpt-5.5 defaults to medium reasoning effort, which serves as the recommended starting point for gpt-5.5's f
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GPT-4.1 Structured CoT Audit: Query, Context, Synthesis
For basic Chain-of-Thought in GPT-4.1, OpenAI recommends placing a step-by-step instruction at the end of the prompt to print document titles and IDs first. To fix systematic eval failures (such as intent misunderstandin
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Triage-to-Arbitration LLM Pipeline With OpenAI Models
OpenAI's official reasoning best practices document a two-stage e-commerce workflow: use fast GPT-4o and GPT-4o-mini models to triage order details with customer information and identify order issues and return policy cl