u/prompts360
Expert · 1265 points · level 5 of 6
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Perplexity Copilot for Underspecified Questions
While Pro Search and Deep Research are ideal for structured research questions requiring wide evidence synthesis, Copilot mode engages the user with clarifying questions before searching, improving scope definition on am
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Perplexity Finance Focus: SEC Filings and Disclosures
Perplexity's Finance Focus Mode is specifically designed for retrieving and analyzing financial data, SEC filings, annual reports, and corporate disclosures. …
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Perplexity Agent API: Keep Instructions Lean
In Perplexity's Agent API, instructions sets the role, tone, formatting, and grounding rules, while input holds the question and seeds the first search query. Because instructions are re-read on every turn of the agent l
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Perplexity Focus Modes: Academic, Writing, Web Workflow
Perplexity's Focus Modes guide recommends combining modes across a multi-layer workflow within a thread—for example, starting in Academic Mode (peer-reviewed databases with APA/MLA citations), switching to Web Mode (real
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Perplexity site: and filetype:pdf Search Operators
Perplexity supports traditional search operators (site:, filetype:) combined directly with natural language synthesis instructions and timeframe filters. …
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Force Mistral JSON Output With Assistant Prefixing
Combine Mistral's JSON output strategy with Prefix Mode ("prefix": True) to guarantee the response starts immediately with { and never includes conversational intro text. …
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Codestral Endpoints: IDE Endpoint vs Main API
Mistral provides two distinct endpoints for Codestral: use codestral.mistral.ai for IDE plugins where users bring their own personal API keys (managed at the personal level and not bound by organization rate limits) for
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Ministral 3B, 8B and 14B for Edge and On-Prem
For compact on-prem or edge deployments requiring both text and vision across a 256k context window, use Ministral 14B ($0.20/1M), Ministral 8B ($0.15/1M), or Ministral 3B ($0.10/1M) with clean Markdown/XML sectioning. …
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Mistral Moderation Thresholds for Inline Guardrails
Configure Mistral's inline moderationllmv2 guardrails (blockonerror: true, ignoreothercategories: false, action: block) with custom category thresholds to enforce automated safety blocking at the API layer. …
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Mistral Medium 3.5 and Small 4: High Reasoning Effort
According to current Mistral provider specifications, mistral-medium-3-5 (best for agentic and coding-heavy workloads) and mistral-small-latest (mistral-small-2603, best for hybrid instruct, reasoning, and coding) suppor
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Mistral prompt_cache_key for Server-Side Prefix Caching
When running repeated queries over a shared system prompt and context across Mistral's 256k context window, configure promptcachekey: shared-prefix to reuse Mistral's server-side prompt cache across requests. …
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Codestral Fill-in-the-Middle (FIM) Prompting
Codestral is fluent in 80+ programming languages and features a dedicated Fill-in-the-Middle (FIM) mechanism. To use FIM effectively, pass the code above the cursor as the prompt (prefix: function definition and docstrin
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Mistral Prefix Mode: Force the Target Language
Mistral's official cookbook documents Prefix Mode: passing an assistant message with "prefix": True prepends a chosen string directly to the model's completion. When user queries or RAG documents are in English but you r
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Run Llama 4 Scout 4-Bit on One 24GB GPU
4-bit quantization compresses Llama 4 Scout's weights by up to 4x, enabling deployment on a single 24GB VRAM card while preserving complex reasoning. Use concise, structured output schemas to maximize throughput on singl
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Llama Stop Tokens: end_of_text vs eot
If EOS (<|endoftext|>) and EOT (<|eot|> or <|eotid|>) tokens aren't handled properly during tokenization and inference, Llama models can keep hallucinating or repeating text indefinitely because they never receive the si
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Llama Guard 4 Prompt Wrapping for Safety Moderation
Pair open-weight Llama 4 and Llama 3.3 deployments with Llama Guard 4 using role-delimited conversation blocks to screen both user inputs and assistant outputs. …
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Llama 4 Scout 10M Context for Regulatory Tracking
According to 2026 Llama engineering guides, Llama 4 Scout features a 10-million-token context window that allows legal compliance systems to monitor entire regulatory databases and notify firms how new 2026 laws affect t
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Llama 4 Vision: How Dynamic Tiling Works
Per Meta's official Llama 4 documentation, multimodal input supports text plus up to 5 images, and image understanding is English-only. Larger images are divided into 336 x 336 pixel local tiles using <|tilexseparator|>
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Llama 4 Tool Calling With the ipython Role
In Llama 4, tool execution outputs are returned to the model using the role tag <|headerstart|>ipython<|headerend|>. In Python zero-shot function calling mode, Llama 4 outputs tool calls formatted as Python function call
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Llama 4 System Prompt to Stop False Refusals
Meta's official Llama 4 documentation provides a suggested system prompt designed to reduce false refusals and "preachy" responses: it instructs the model to be companionable and confident, avoid over-helpfulness on simp
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Llama 4 vs Llama 3 Special Tokens: Exact Prompt Format
According to Meta's official model cards, Llama 3 used <|startheaderid|>{role}<|endheaderid|> and <|eotid|>, whereas Llama 4 uses <|headerstart|>{role}<|headerend|> to enclose the role (system, user, assistant, ipython)
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DeepSeek-V3 Zero-Shot CoT Trigger Phrase
Prompt engineering research shows that on non-reasoning models, the specific Zero-Shot CoT trigger variant "Let's work this out in a step by step way to be sure we have the right answer" outperforms minimalist triggers o
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Stop DeepSeek-R1 Switching Languages Mid-Answer
While DeepSeek-R1 was trained via multi-stage SFT and GRPO to address the poor readability and language-switching of DeepSeek-R1-Zero, studies note it can still provide different responses displaying different cultural v
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DeepSeek-V3 vs R1 for Long-Context Workloads
Empirical evaluations note that DeepSeek-R1 is slightly less effective in massive long-context scenarios than state-of-the-art non-reasoning LLMs and occasionally becomes overwhelmed by increasingly long contexts, output
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Parse DeepSeek-R1 think Tags for Reasoning Audits
DeepSeek-R1 utilizes tokens specifically for reflecting on how to respond to the user prompt, displayed inside <think> tags in the output. Programmatically separate the <think> block from the final answer so auditors can