u/prompts360
Expert · 1265 points · level 5 of 6
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Document Non-Obvious Conventions for AI Coding Agents
Use your config file to state critical, non-obvious conventions the AI might miss from looking at a single file: named vs. default exports, standard API response shapes, migration file locations, and environment variable
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Map a Monorepo in AGENTS.md
Provide a high-level mapping of directories in AGENTS.md—especially for monorepos—separating apps, shared packages, and infrastructure. …
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Pin Your Tech Stack and Versions in Agent Rules
Including your exact framework, language, styling tools, database, and deployment target versions in AGENTS.md or CLAUDE.md prevents the AI from guessing legacy syntax. …
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GitHub Copilot Prompt Files in .github/prompts
Keep single-use task instructions out of persistent config files and store reusable workflows in a custom prompts/ folder inside the dot directory so you can trigger them manually in chat via @prompts/my-custom-workflow.
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Cursor Model Decision Scope: Prevent Context Bloat
Use Cursor's Model Decision activation scope with a clear rule description so specialized rules are loaded only when the AI determines the task requires them. File: .cursor/rules/chart-components.mdc (Scope: Model Decisi
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AGENTS.override.md for Local Developer Overrides
Codex CLI walks from the project root down to the current working directory checking for AGENTS.override.md alongside AGENTS.md at every level for uncommitted, machine-specific, or personal developer overrides. Never com
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Perplexity Computer With Snowflake: Revenue Decomposition
Perplexity Computer acts as an AI orchestrator that plans, researches, and delivers multi-step work—including connecting directly to Snowflake data warehouses to automate revenue growth decomposition and launch shareable
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Perplexity Video Focus: Summaries and Timestamps
Perplexity's Video Focus Mode searches and analyzes content from video platforms like YouTube, providing detailed summaries, key points, and relevant timestamps. …
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Perplexity Social Focus: Public Sentiment Research
Switch to Social Focus Mode (or Reddit focus) to restrict retrieval to social media platforms, online forums, and discussion boards for real-time discussions and public sentiment. …
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Perplexity API Filters: Use Parameters Not Prose
Perplexity's documentation emphasizes that API tool config parameters (such as recency filters for last day, week, month, or year) are applied by the search backend on every call, whereas prose-based filters inside the p
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Perplexity API Prompts: Target-Page Vocabulary, Result Caps
Perplexity's Agent API guide notes that specificity in input directly improves retrieval because it seeds the first search query: include vocabulary that actually appears on relevant target pages, add words to disambigua
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Mistral Classification: Single-Word Label vs JSON
Mistral's official prompting guide outlines two main classification strategies: (1) ask for the label directly as a single word/string (fast, cheap, uses the fewest output tokens), or (2) ask for a JSON object (generates
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Voxtral Small: Audio-Aware Chat Prompts
Use Mistral's voxtral-small-2507 (32k context window, supporting text and native audio inputs at $0.10/1M tokens + $0.004/audio minute) for audio-aware chat and voice support analysis. …
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Lean 4 Proofs With Leanstral
Mistral provides labs-leanstral-1-5 (256k context window with text and tools support) specifically for Lean 4 proof engineering. Prompt it to synthesize formal tactics and verify lemmas. …
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Replace Vague Adjectives in Mistral Prompts
Mistral's official prompting guide explicitly instructs developers to avoid subjective and blurry words: replace quantitative adjectives like "too long", "too short", "many", "few" with objective measures, and replace bl
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Mistral Multi-Character Orchestration via Prefix Rotation
As shown in Mistral's prefix cookbook, you can orchestrate multi-character conversations across dynamic chat loops by selecting a persona (e.g., random.choice(["Shakespeare", "Einstein", "Batman"]) or a product review pa
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Mistral Roleplay Locks Without Meta-Commentary
To prevent Mistral from generating multi-turn back-and-forth scripts or adding meta-disclaimers during character roleplay, Mistral's cookbook recommends pairing a single-reply system instruction with a "CharacterName: "
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Mistral Prefix Sandwich Against Jailbreaks
While users can sometimes bypass system prompts with adversarial prompt engineering, Mistral's official documentation notes that prefixes make it much harder, and sometimes almost impossible, to break safety boundaries b
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Mistral Persona Prefix: 3 Words, Fewer Tokens
Detailed system prompts explaining persona, tone, and language consume significant prompt tokens. Mistral's prefix cookbook shows that a concise 3-word prefix such as prefix = "Assistant Pirate Français : " implicitly co
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Llama 3.3 Multi-Turn Roles With eot_id
In multi-turn Llama 3/3.3 conversations, enclosing each speaker role in <|startheaderid|>...<|endheaderid|> and terminating every turn with <|eotid|> allows the model to track roles and context across multiple exchanges
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Domain Routing in Llama 4 Maverick's MoE
Technical guides note that Llama 4 Maverick uses a sparse activation system storing 400B total parameters while activating roughly 17B parameters per token. Anchor the system prompt with precise domain terminology to ali
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Llama 4 Base Model Few-Shot Completion Prompts
When prompting a pretrained (non-Instruct) Llama 4 model for direct text completion, omit all <|headerstart|> role tags and pass <|beginoftext|> followed directly by your text completion pattern. …
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Llama 3 and 3.3 Prompt Headers: Keep the Newlines
Meta's official Llama 3 documentation highlights that newlines (0x0A) are part of the prompt format, and the prompt must always end with the last user message followed by the assistant header <|startheaderid|>assistant<|
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Fine-Tune Llama 4 Scout With QLoRA Padding Settings
Fine-tuning guides for Llama 4 Scout note that despite its 10M context window, training sequences should be truncated to 1024 or 2048 tokens with 4-bit quantization (allowing Scout to run on a 24GB VRAM card), paddingsid
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Llama 4 Multi-Image Comparison Prompt
To analyze multiple images (up to 5) in Llama 4, pass each image sequentially within its own <|imagestart|>...<|imageend|> block prior to the English text prompt. …