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Posts about Search

<h2><br /></h2>
<p>We all know that AI is moving fast. Enterprises want to harness the power of advanced models to analyze data, generate content, and help teams move faster. </p><p>But getting there? Not so easy.</p><p>Today, the capabilities required to work with modern AI models are scattered across tools, APIs, and pricing structures. Context is difficult to maintain, engineers spend time stitching together services, and leadership teams struggle with governance and cost control.</p>
<p>The result is fragmentation. In fact, <a href="https://www.forbes.com/sites/jasonsnyder/2025/08/26/mit-finds-95-of-genai-pilots-fail-because-companies-avoid-friction/">a widely cited MIT study </a>found that <strong>95% of AI projects never reach production</strong>. But t<span style="font-size:1rem">he problem isn’t a lack of models. Organizations need a </span><strong>unified interface</strong><span style="font-size:1rem"> that makes it easier to work with AI across modalities.</span></p>
<p>That’s why <strong>UI is the next frontier of AI – and Osirus is the answer.</strong></p><h2><span style="font-size:2rem;font-weight:600">Introducing 'The UI for Gemini'</span></h2>
<p><span style="font-size:1rem">Now generally available for organizations that want a focused environment built specifically around the </span><strong>Google Gemini family of AI models</strong><span style="font-size:1rem">, Osirus provides a streamlined interface for working with the most powerful models in the Gemini ecosystem, including <b>Google Gemini for text, Nano Banana for images, and Veo for video.</b></span></p>
<p>Instead of juggling multiple tools and APIs, teams gain a <strong>single UI designed to unlock the full capabilities of Gemini – </strong>making it easier to experiment, build, and deploy AI-powered workflows.</p>
<p>The name <strong>Osirus</strong> draws inspiration from humanity’s greatest engineering achievements. Like the thinking that made the pyramids possible, Osirus was designed to unlock a powerful source of intelligence, insight, and guidance for the AI era.</p><h2><span style="font-size:2rem;font-weight:600">AI Across Every Modality</span></h2>
<p>The future of AI is <strong>multimodal, <b>and o</b></strong><span style="font-size:1rem">rganizations increasingly need systems that work across </span><strong>chat, search, images, video, and speech</strong><span style="font-size:1rem">, while maintaining context across projects and data sources.</span></p>
<p>Osirus brings these capabilities together through a UI built specifically for Gemini. Teams can easily select the best model for a task, analyze files, generate text, create images and video, or work with multimodal data – all within a single interface.</p>
<p>Generate images with natural prompts. Analyze documents and datasets. Switch models instantly to explore different outputs. Everything happens within a cohesive experience designed around the strengths of the Gemini model family.</p>
<p>Osirus provides the <strong>multimodal toolkit organizations need to unlock the full power of Gemini AI</strong>.</p><h2><span style="font-size:2rem;font-weight:600">Built for the Gemini Ecosystem</span></h2>
<p>Osirus is designed to work seamlessly with the <strong>Google Gemini model family</strong>, providing a dedicated interface for interacting with the latest capabilities in generative and multimodal AI.</p>
<p>By focusing on a single AI provider, Osirus offers a <strong>clean, streamlined experience</strong> that allows teams to get the most from Gemini’s advanced models without the complexity of managing multiple platforms.</p>
<p>The result is a powerful yet intuitive environment where organizations can explore AI, generate insights, and integrate multimodal intelligence into their workflows.</p><h2><span style="font-size:2rem;font-weight:600">Empowering Communities and Enterprises</span></h2>
<p>Organizations across industries – from enterprises to mission-driven institutions – are exploring how AI can improve decision-making and unlock new insights. <span style="font-size:1rem">Osirus makes it easier for teams to begin working with Gemini models in a secure, controlled environment where projects, outputs, and data remain organized and accessible.</span></p><p>The exciting news is that we already have interest from mission-focused organizations focused on serving citizens across communities. Digital records are public records, and if agencies want to adopt AI, they need a system of record to ensure real governance. This is where Osirus can make a significant impact.</p>
<p>By providing a dedicated UI for Gemini, Osirus helps organizations move beyond experimentation and begin realizing the practical value of AI. <span style="font-size:1rem;color:rgb(15, 20, 26);font-family:&quot;Amazon Ember&quot;, &quot;Helvetica Neue&quot;, Roboto, Arial, sans-serif">Get started at </span><a target="_blank" href="https://awsnova.osirus.ai/" style="font-size:1rem;border-collapse:separate;border-spacing:0px;caption-side:top;cursor:auto;direction:inherit;empty-cells:show;font-variant-numeric:normal;font-variant-east-asian:normal;font-variant-alternates:normal;font-variant-position:normal;font-variant-emoji:normal;font-stretch:normal;hyphens:none;list-style:outside none disc;tab-size:8;text-shadow:none;visibility:visible;line-height:24px;font-family:&quot;Amazon Ember&quot;, &quot;Helvetica Neue&quot;, Roboto, Arial, sans-serif;-webkit-font-smoothing:auto;display:inline;--awsui-style-focus-ring-box-shadow-n6lfw8:0 0 0 2px #7300E5;text-underline-offset:0.25em;text-decoration-thickness:1px;color:rgb(15, 20, 26);text-decoration-line:underline;text-decoration-color:currentcolor;transition-property:color, text-decoration, -webkit-text-decoration;transition-duration:0.165s"><span style="font-weight:bolder">https://gemini.osirus.ai/</span></a></p><p><span style="color:rgb(35, 49, 54);font-family:Circular, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif">#GeminiLiveAgentChallenge</span></p>

Introducing Osirus AI for Google: The UI for Gemini

<p><strong>Ready for the "max" on search? Then MiniMax M2</strong> on <strong>Bedrock</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI. <span style="font-size:1rem">This model can be integrated into Osirus workflows for iterative prompt development and app-specific automation.</span></p>
    <h4>What you can build with this model</h4>
    <ul><li>Answer synthesis flows that produce short executive summaries plus detail blocks.</li><li>Competitive monitoring briefs generated from recurring search snapshots.</li><li>Grounded research workflows that blend internal docs with web context.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Supports rapid testing of prompt strategy and output structure.</li><li>Useful for turning manual team workflows into reusable AI runbooks.</li><li>Flexible for prototype-to-production iteration loops.</li><li>Strong fit for retrieval synthesis and decision-support style responses.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>MiniMax M2</strong> from <strong>Bedrock</strong>.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Rewrite this user query into five focused search queries for deeper retrieval coverage.</li><li>Given these retrieved passages, produce an answer with assumptions, evidence, and confidence level.</li><li>Create an executive summary from search results with one-paragraph recommendations.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Define quality criteria early and evaluate outputs against those criteria.</li></ul>
    <p><a href="/providers/aws/models/minimax.minimax-m2">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search Ideas for MiniMax M2 with Amazon Bedrock

matt

<p><strong>Trouble searching in the dark? Illuminate your experiences with Gemini Flash-Lite! The latest version</strong> on <strong>Google</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Gemini models are designed for multimodal understanding and broad workflow flexibility.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Grounded research workflows that blend internal docs with web context.</li><li>Query rewriting pipelines that improve recall before retrieval.</li><li>Answer synthesis flows that produce short executive summaries plus detail blocks.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Useful for long-context planning and synthesis style tasks.</li><li>Good for mixed text-and-media reasoning workflows.</li><li>Adaptable across assistant, research, and generation use cases.</li><li>Effective for turning broad retrieval into concise strategic briefs.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Gemini Flash-Lite Latest</strong> from <strong>Google</strong>.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Create an executive summary from search results with one-paragraph recommendations.</li><li>Compare the top three sources and list agreement points, conflicts, and missing data.</li><li>Rewrite this user query into five focused search queries for deeper retrieval coverage.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Define output contract clearly when chaining multiple generation stages.</li></ul>
    <p><a href="/providers/google/models/gemini-flash-lite-latest">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search Ideas for Google Gemini Flash-Lite

<p><strong>Listen up! Gemini 2.5 Flash Native Audio Latest</strong> on <strong>Google</strong> is a practical but awesome option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Gemini models are designed for multimodal understanding and broad workflow flexibility.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Analyst workflows that compare multiple sources and reconcile contradictions.</li><li>Structured report generation from retrieved passages and citation snippets.</li><li>Answer synthesis flows that produce short executive summaries plus detail blocks.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Adaptable across assistant, research, and generation use cases.</li><li>Useful for long-context planning and synthesis style tasks.</li><li>Good for mixed text-and-media reasoning workflows.</li><li>Effective for turning broad retrieval into concise strategic briefs.</li><li>Model outputs include: Text, Audio.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Gemini 2.5 Flash Native Audio Latest</strong> from <strong>Google</strong>.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Draft a research brief with priorities: speed, reliability, and implementation effort.</li><li>Build a fact table from the retrieved content and include open questions.</li><li>Rewrite this user query into five focused search queries for deeper retrieval coverage.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Add source freshness expectations for time-sensitive topics.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Define output contract clearly when chaining multiple generation stages.</li></ul>
    <p><a href="/providers/google/models/gemini-2.5-flash-native-audio-latest">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Google Gemini 2.5 Flash Native Audio


    <p><strong>Claude Sonnet 4.5</strong> on <strong>Anthropic</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Claude Sonnet is commonly used as a balanced tier for quality, speed, and versatility.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Grounded research workflows that blend internal docs with web context.</li><li>Query rewriting pipelines that improve recall before retrieval.</li><li>Answer synthesis flows that produce short executive summaries plus detail blocks.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Good for day-to-day assistants handling mixed complexity.</li><li>Strong all-around performance for product and developer workflows.</li><li>Suitable for structured business outputs and implementation plans.</li><li>Strong for concise synthesis over mixed retrieval inputs.</li><li>Runs as a connected provider model in Osirus after provider setup.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Claude Sonnet 4.5</strong> from <strong>Anthropic</strong>.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Create an executive summary from search results with one-paragraph recommendations.</li><li>Compare the top three sources and list agreement points, conflicts, and missing data.</li><li>Rewrite this user query into five focused search queries for deeper retrieval coverage.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Add grounding/context blocks to avoid over-generalized responses.</li></ul>
    <p><a href="/providers/anthropic/models/claude-sonnet-4-5-20250929">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Claude Sonnet 4.5 (Anthropic)


    <p><strong>Claude Opus 4.6</strong> on <strong>Anthropic</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Claude Opus is generally positioned for deep reasoning and high-accuracy analysis workloads.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Query rewriting pipelines that improve recall before retrieval.</li><li>Grounded research workflows that blend internal docs with web context.</li><li>Competitive monitoring briefs generated from recurring search snapshots.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Performs well in dense, high-context analytical tasks.</li><li>Useful for policy-heavy workflows where nuance and precision matter.</li><li>Strong candidate for critical reviews and complex synthesis.</li><li>Useful for complex evidence reconciliation across many sources.</li><li>Runs as a connected provider model in Osirus after provider setup.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Claude Opus 4.6</strong> from <strong>Anthropic</strong>.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Compare the top three sources and list agreement points, conflicts, and missing data.</li><li>Create an executive summary from search results with one-paragraph recommendations.</li><li>Given these retrieved passages, produce an answer with assumptions, evidence, and confidence level.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Use explicit acceptance criteria for long-form outputs and summaries.</li></ul>
    <p><a href="/providers/anthropic/models/claude-opus-4-6">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Claude Opus 4.6 (Anthropic)


    <p><strong>Qwen/Qwen3-4B-Instruct-2507</strong> on <strong>Hugging Face</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Open-model families provide flexible options for cost, speed, and specialization tradeoffs.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Structured report generation from retrieved passages and citation snippets.</li><li>Analyst workflows that compare multiple sources and reconcile contradictions.</li><li>Query rewriting pipelines that improve recall before retrieval.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Good for tailored workflows where teams tune prompts per domain.</li><li>Useful for balancing throughput and output quality across workloads.</li><li>Strong choice for experimentation across multiple model variants.</li><li>Useful for query expansion and evidence summarization pipelines.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Qwen/Qwen3-4B-Instruct-2507</strong> from <strong>Hugging Face</strong>.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Build a fact table from the retrieved content and include open questions.</li><li>Draft a research brief with priorities: speed, reliability, and implementation effort.</li><li>Compare the top three sources and list agreement points, conflicts, and missing data.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Benchmark several variants for your exact task before locking defaults.</li></ul>
    <p><a href="/providers/huggingface/models/Qwen%2FQwen3-4B-Instruct-2507">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Qwen/Qwen3-4B-Instruct-2507 (Hugging Face)

<p><strong>Code up to what's next! Qwen3 Coder Next</strong> on <strong>Amazon Bedrock</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI. <span style="font-size:1rem">Open-model families provide flexible options for cost, speed, and specialization tradeoffs.</span></p>
    <h4>What you can build with this model</h4>
    <ul><li>Analyst workflows that compare multiple sources and reconcile contradictions.</li><li>Structured report generation from retrieved passages and citation snippets.</li><li>Grounded research workflows that blend internal docs with web context.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Useful for balancing throughput and output quality across workloads.</li><li>Good for tailored workflows where teams tune prompts per domain.</li><li>Strong choice for experimentation across multiple model variants.</li><li>Useful for query expansion and evidence summarization pipelines.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Qwen3 Coder Next</strong> from <strong>Bedrock</strong>.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Draft a research brief with priorities: speed, reliability, and implementation effort.</li><li>Build a fact table from the retrieved content and include open questions.</li><li>Create an executive summary from search results with one-paragraph recommendations.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Separate retrieval quality checks from answer quality checks.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Benchmark several variants for your exact task before locking defaults.</li></ul>
    <p><a href="/providers/aws/models/qwen.qwen3-coder-next">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search Ideas for Qwen3 Coder Next for Amazon Bedrock


    <p><strong>Gemini 2.5 Flash Native Audio Preview 12-2025</strong> on <strong>Google</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Gemini models are designed for multimodal understanding and broad workflow flexibility.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Query rewriting pipelines that improve recall before retrieval.</li><li>Grounded research workflows that blend internal docs with web context.</li><li>Competitive monitoring briefs generated from recurring search snapshots.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Good for mixed text-and-media reasoning workflows.</li><li>Useful for long-context planning and synthesis style tasks.</li><li>Adaptable across assistant, research, and generation use cases.</li><li>Effective for turning broad retrieval into concise strategic briefs.</li><li>Model outputs include: Text, Audio.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Gemini 2.5 Flash Native Audio Preview 12-2025</strong> from <strong>Google</strong>.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Compare the top three sources and list agreement points, conflicts, and missing data.</li><li>Create an executive summary from search results with one-paragraph recommendations.</li><li>Given these retrieved passages, produce an answer with assumptions, evidence, and confidence level.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Define output contract clearly when chaining multiple generation stages.</li></ul>
    <p><a href="/providers/google/models/gemini-2.5-flash-native-audio-preview-12-2025">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Gemini 2.5 Flash Native Audio Preview 12-2025 (Google)


    <p><strong>gpt-3.5-turbo</strong> on <strong>OpenAI</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>This model can be integrated into Osirus workflows for iterative prompt development and app-specific automation.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Analyst workflows that compare multiple sources and reconcile contradictions.</li><li>Structured report generation from retrieved passages and citation snippets.</li><li>Grounded research workflows that blend internal docs with web context.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Useful for turning manual team workflows into reusable AI runbooks.</li><li>Flexible for prototype-to-production iteration loops.</li><li>Supports rapid testing of prompt strategy and output structure.</li><li>Strong fit for retrieval synthesis and decision-support style responses.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>gpt-3.5-turbo</strong> from <strong>OpenAI</strong>.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Draft a research brief with priorities: speed, reliability, and implementation effort.</li><li>Build a fact table from the retrieved content and include open questions.</li><li>Create an executive summary from search results with one-paragraph recommendations.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Separate retrieval quality checks from answer quality checks.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Define quality criteria early and evaluate outputs against those criteria.</li></ul>
    <p><a href="/providers/openai/models/gpt-3.5-turbo">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for gpt-3.5-turbo (OpenAI)


    <p><strong>Gemini 2.5 Flash-Lite</strong> on <strong>Google</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Gemini models are designed for multimodal understanding and broad workflow flexibility.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Grounded research workflows that blend internal docs with web context.</li><li>Query rewriting pipelines that improve recall before retrieval.</li><li>Answer synthesis flows that produce short executive summaries plus detail blocks.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Useful for long-context planning and synthesis style tasks.</li><li>Good for mixed text-and-media reasoning workflows.</li><li>Adaptable across assistant, research, and generation use cases.</li><li>Effective for turning broad retrieval into concise strategic briefs.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Gemini 2.5 Flash-Lite</strong> from <strong>Google</strong>.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Create an executive summary from search results with one-paragraph recommendations.</li><li>Compare the top three sources and list agreement points, conflicts, and missing data.</li><li>Rewrite this user query into five focused search queries for deeper retrieval coverage.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Define output contract clearly when chaining multiple generation stages.</li></ul>
    <p><a href="/providers/google/models/gemini-2.5-flash-lite">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Gemini 2.5 Flash-Lite (Google)


    <p><strong>Qwen/Qwen2.5-Coder-32B-Instruct</strong> on <strong>Hugging Face</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Open-model families provide flexible options for cost, speed, and specialization tradeoffs.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Structured report generation from retrieved passages and citation snippets.</li><li>Analyst workflows that compare multiple sources and reconcile contradictions.</li><li>Competitive monitoring briefs generated from recurring search snapshots.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Strong choice for experimentation across multiple model variants.</li><li>Good for tailored workflows where teams tune prompts per domain.</li><li>Useful for balancing throughput and output quality across workloads.</li><li>Useful for query expansion and evidence summarization pipelines.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Qwen/Qwen2.5-Coder-32B-Instruct</strong> from <strong>Hugging Face</strong>.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Track retrieval hit quality and revise prompts for weak evidence cases.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Build a fact table from the retrieved content and include open questions.</li><li>Draft a research brief with priorities: speed, reliability, and implementation effort.</li><li>Given these retrieved passages, produce an answer with assumptions, evidence, and confidence level.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Enforce citation rules for high-stakes summaries.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Log query rewrites and measure which rewrites improve source relevance.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Benchmark several variants for your exact task before locking defaults.</li></ul>
    <p><a href="/providers/huggingface/models/Qwen%2FQwen2.5-Coder-32B-Instruct">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Qwen/Qwen2.5-Coder-32B-Instruct (Hugging Face)


    <p><strong>Qwen/Qwen2.5-Coder-1.5B-Instruct</strong> on <strong>Hugging Face</strong> is a practical option for <strong>Search</strong> workflows in Osirus AI.</p>
    <p>Open-model families provide flexible options for cost, speed, and specialization tradeoffs.</p>
    <h4>What you can build with this model</h4>
    <ul><li>Answer synthesis flows that produce short executive summaries plus detail blocks.</li><li>Competitive monitoring briefs generated from recurring search snapshots.</li><li>Grounded research workflows that blend internal docs with web context.</li></ul>
    <h4>Why this model is a good fit</h4>
    <ul><li>Strong choice for experimentation across multiple model variants.</li><li>Useful for balancing throughput and output quality across workloads.</li><li>Good for tailored workflows where teams tune prompts per domain.</li><li>Useful for query expansion and evidence summarization pipelines.</li><li>Model outputs include: Text.</li></ul>
    <h4>Build flow in Osirus UI</h4>
    <ol><li>Open <code>/search</code> in Osirus and select <strong>Qwen/Qwen2.5-Coder-1.5B-Instruct</strong> from <strong>Hugging Face</strong>.</li><li>Define retrieval scope (docs, web, or blended) before prompt tuning.</li><li>Add a query-rewrite stage for better recall and cleaner evidence.</li><li>Force answer sections for findings, evidence, and unresolved questions.</li><li>Save the final workflow as a repeatable pattern for your team.</li></ol>
    <h4>Starter prompts</h4>
    <ul><li>Rewrite this user query into five focused search queries for deeper retrieval coverage.</li><li>Given these retrieved passages, produce an answer with assumptions, evidence, and confidence level.</li><li>Create an executive summary from search results with one-paragraph recommendations.</li></ul>
    <h4>Production checklist</h4>
    <ul><li>Evaluate outputs against completeness and contradiction metrics.</li><li>Add source freshness expectations for time-sensitive topics.</li><li>Enforce citation rules for high-stakes summaries.</li><li>Separate retrieval quality checks from answer quality checks.</li><li>Benchmark several variants for your exact task before locking defaults.</li></ul>
    <p><a href="/providers/huggingface/models/Qwen%2FQwen2.5-Coder-1.5B-Instruct">Open this model in Osirus</a> and turn one of these ideas into a reusable team workflow.</p>

Search ideas for Qwen/Qwen2.5-Coder-1.5B-Instruct (Hugging Face)

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