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    <title>LLM APIs and prompting guides · The Tech Gazette</title>
    <link>https://techgazette.com.br/learn/prompting/</link>
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    <description>New guides on llm apis and prompting, by the people who build it. Talking to a model well, from your own code.</description>
    <language>en</language>
    <ttl>180</ttl>
    <lastBuildDate>Thu, 15 Oct 2026 18:00:00 GMT</lastBuildDate>
    <item>
      <title>Scale Agentic Coding Workflows with Meta’s Muse Spark Open on NVIDIA GB300 NVL72</title>
      <link>https://developer.nvidia.com/blog/scale-agentic-coding-workflows-with-metas-muse-spark-open-on-nvidia-gb300-nvl72/</link>
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      <pubDate>Thu, 15 Oct 2026 18:00:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>NVIDIA Developer — Meta launches Muse Spark Open, a coding-optimized 816B mixture-of-experts (MoE) model with 42B active parameters and a 1M token context window. Trained on...</description>
    </item>
    <item>
      <title>Case Study: How CodeRabbit Leverages LanceDB for AI-Powered Code Reviews</title>
      <link>https://www.lancedb.com/blog/case-study-coderabbit</link>
      <guid isPermaLink="true">https://www.lancedb.com/blog/case-study-coderabbit</guid>
      <pubDate>Sat, 10 Oct 2026 04:06:00 GMT</pubDate>
      <dc:creator>LanceDB</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>LanceDB — How CodeRabbit leverages LanceDB-powered context engineering turns every review into a quality breakthrough.</description>
    </item>
    <item>
      <title>Generate Images in Python With OpenAI's GPT Image API</title>
      <link>https://realpython.com/generate-images-openai/</link>
      <guid isPermaLink="true">https://realpython.com/generate-images-openai/</guid>
      <pubDate>Fri, 09 Oct 2026 14:00:00 GMT</pubDate>
      <dc:creator>Real Python</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Real Python — Learn how to generate images from text prompts with OpenAI's GPT Image API in Python, tune their size and quality, and edit them with a follow-up prompt.</description>
    </item>
    <item>
      <title>OpenTelemetry Collector Configuration for LLM Observability</title>
      <link>https://www.honeycomb.io/blog/otel-collector-llm-observability</link>
      <guid isPermaLink="true">https://www.honeycomb.io/blog/otel-collector-llm-observability</guid>
      <pubDate>Wed, 07 Oct 2026 13:00:00 GMT</pubDate>
      <dc:creator>Honeycomb</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Honeycomb — A complete, annotated OpenTelemetry Collector configuration for LLM observability: receiving traces over OTLP, normalizing OpenInference/OpenLLMetry schemas onto GenAI semantic conventions, redacting sensitive prompt and…</description>
    </item>
    <item>
      <title>Why does cosine similarity fail for semantic caching? Testing cosine, LLM prompts, and a purpose-built verifier: Jev</title>
      <link>https://www.digitalocean.com/community/tutorials/semantic-cache-verification-jev-vs-cosine-similarity</link>
      <guid isPermaLink="true">https://www.digitalocean.com/community/tutorials/semantic-cache-verification-jev-vs-cosine-similarity</guid>
      <pubDate>Wed, 07 Oct 2026 11:00:00 GMT</pubDate>
      <dc:creator>DigitalOcean Tutorials</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>DigitalOcean Tutorials — We tested cosine similarity, prompted LLMs, and a purpose-built verifier (Jev) on 500 labeled query pairs, and cosine was clearly the least accurate, serving near-misses like &quot;capital of Kenya&quot; for &quot;capital of Jamaica.&quot;…</description>
    </item>
    <item>
      <title>WTF is context engineering? (with real examples)</title>
      <link>https://posthog.com/blog/wtf-is-context-engineering</link>
      <guid isPermaLink="true">https://posthog.com/blog/wtf-is-context-engineering</guid>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>PostHog</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>PostHog — Say you're deploying an AI assistant that processes online order returns. For it to work, it would need access to your store's purchase policy, item…</description>
    </item>
    <item>
      <title>The Hidden Line in the Footer: How to Stop AI Agents From Following Bad Instructions</title>
      <link>https://www.anaconda.com/blog/indirect-prompt-injection-ai-agents</link>
      <guid isPermaLink="true">https://www.anaconda.com/blog/indirect-prompt-injection-ai-agents</guid>
      <pubDate>Mon, 05 Oct 2026 14:16:00 GMT</pubDate>
      <dc:creator>Anaconda</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Anaconda — A hidden line of text can trick an AI agent into leaking data or sending emails. Learn how indirect prompt injection works and how guardrails stop it in time. The post The Hidden Line in the Footer: How to Stop AI Agents…</description>
    </item>
    <item>
      <title>AI Model Management in Production: A Practical Workflow</title>
      <link>https://launchdarkly.com/blog/ai-model-management/</link>
      <guid isPermaLink="true">https://launchdarkly.com/blog/ai-model-management/</guid>
      <pubDate>Sat, 03 Oct 2026 13:55:00 GMT</pubDate>
      <dc:creator>LaunchDarkly</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>LaunchDarkly — Manage AI models, prompts, evaluations, rollouts, and production performance with a practical AI model management workflow.</description>
    </item>
    <item>
      <title>Prompt caching benchmark: high cache reuse doesn’t always mean lower cost</title>
      <link>https://arize.com/blog/prompt-caching-benchmark/</link>
      <guid isPermaLink="true">https://arize.com/blog/prompt-caching-benchmark/</guid>
      <pubDate>Fri, 02 Oct 2026 18:01:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Arize — We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping assistant agent. The…</description>
    </item>
    <item>
      <title>A model guide for the GPT-6 family</title>
      <link>https://openai.com/index/practical-guide-building-gpt-6</link>
      <guid isPermaLink="true">https://openai.com/index/practical-guide-building-gpt-6</guid>
      <pubDate>Fri, 02 Oct 2026 16:15:00 GMT</pubDate>
      <dc:creator>OpenAI</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>OpenAI — Learn how startups can choose GPT-6 models, tune reasoning effort, improve prompts and skills, coordinate tools, and prepare workflows for production.</description>
    </item>
    <item>
      <title>Build Your App One Feature at a Time (Bolt.new Guide)</title>
      <link>https://bolt.new/blog/build-app-one-feature-at-a-time</link>
      <guid isPermaLink="true">https://bolt.new/blog/build-app-one-feature-at-a-time</guid>
      <pubDate>Fri, 02 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Bolt</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Bolt — Build your app in layers: one feature at a time, so every change is easy to test, undo, and fix. The order to build in, with prompts for each layer.</description>
    </item>
    <item>
      <title>Building ambient agents with Amazon Bedrock AgentCore: From event-driven signals to human-in-the-loop workflows</title>
      <link>https://aws.amazon.com/blogs/machine-learning/building-ambient-agents-with-amazon-bedrock-agentcore-from-event-driven-signals-to-human-in-the-loop-workflows/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/building-ambient-agents-with-amazon-bedrock-agentcore-from-event-driven-signals-to-human-in-the-loop-workflows/</guid>
      <pubDate>Thu, 01 Oct 2026 16:40:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>AWS Machine Learning — Ambient agents respond to events such as an Amazon S3 upload, a schedule, or an alert instead of waiting for a chat prompt. This post walks through building framework-agnostic ambient agents on Amazon Bedrock AgentCore u…</description>
    </item>
    <item>
      <title>Under the Hood: Prompt tuning Shake to Summarize for recipes</title>
      <link>https://blog.mozilla.org/en/firefox/firefox-ai/prompt-tuning-firefox-shake-to-summarize-recipes/</link>
      <guid isPermaLink="true">https://blog.mozilla.org/en/firefox/firefox-ai/prompt-tuning-firefox-shake-to-summarize-recipes/</guid>
      <pubDate>Thu, 01 Oct 2026 16:00:00 GMT</pubDate>
      <dc:creator>Mozilla</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Mozilla — Following the rollout of Firefox’s Shake to Summarize feature to Android devices this May, we wanted to take a closer look at the modeling work behind the feature. In a previous blog post, we discussed our model selectio…</description>
    </item>
    <item>
      <title>Genspark protects live agent sessions with Azure Cosmos DB Global Secondary Indexes</title>
      <link>https://devblogs.microsoft.com/cosmosdb/genspark-protects-live-agent-sessions-with-azure-cosmos-db-global-secondary-indexes/</link>
      <guid isPermaLink="true">https://devblogs.microsoft.com/cosmosdb/genspark-protects-live-agent-sessions-with-azure-cosmos-db-global-secondary-indexes/</guid>
      <pubDate>Thu, 01 Oct 2026 16:00:00 GMT</pubDate>
      <dc:creator>Azure Cosmos DB</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Azure Cosmos DB — This article was authored by Justin Liu, co-founder and chief architect, Genspark. The work does not end when the prompt does A user comes to Genspark with a goal: research a market, build a presentation, produce a repor…</description>
    </item>
    <item>
      <title>How uniopen customized Amazon Nova to their retail moderation policies for production deployment</title>
      <link>https://aws.amazon.com/blogs/machine-learning/how-uniopen-customized-amazon-nova-to-their-retail-moderation-policies-for-production-deployment/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/how-uniopen-customized-amazon-nova-to-their-retail-moderation-policies-for-production-deployment/</guid>
      <pubDate>Thu, 01 Oct 2026 15:33:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>AWS Machine Learning — See how uniopen, a retail platform from Taiwan's Uni-President Enterprises Group, adapted Amazon Nova 2 Lite to its content-moderation policies using supervised fine-tuning in Amazon SageMaker AI and prompt optimization.…</description>
    </item>
    <item>
      <title>How Jev Compares to Other Rerankers</title>
      <link>https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers</link>
      <guid isPermaLink="true">https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers</guid>
      <pubDate>Thu, 01 Oct 2026 03:58:00 GMT</pubDate>
      <dc:creator>LanceDB</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>LanceDB — See how Jev performs in a benchmark of 19 reranker configurations across five datasets, covering accuracy, latency, and prompt-driven relevance for LanceDB search.</description>
    </item>
    <item>
      <title>How Deterministic Controls Turn AI Output Into Reliable Prompt Templates</title>
      <link>https://engineering.salesforce.com/how-deterministic-controls-turn-ai-output-into-reliable-prompt-templates/</link>
      <guid isPermaLink="true">https://engineering.salesforce.com/how-deterministic-controls-turn-ai-output-into-reliable-prompt-templates/</guid>
      <pubDate>Wed, 30 Sep 2026 13:38:00 GMT</pubDate>
      <dc:creator>Salesforce Engineering</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Salesforce Engineering — By Vaibhav Raizada, Kumar Kasimala, and Ashish Gite. In our Engineering Energizers Q&amp;A series, we highlight the engineering minds driving innovation across Salesforce. Today, we spotlight Vaibhav Raizada, a senior softwa…</description>
    </item>
    <item>
      <title>Stripe’s Payment Method Factory: Orchestrating agents for repeated, custom integrations</title>
      <link>https://stripe.dev/blog/stripes-payment-method-factory-orchestrating-agents-for-repeated-custom-integrations</link>
      <guid isPermaLink="true">https://stripe.dev/blog/stripes-payment-method-factory-orchestrating-agents-for-repeated-custom-integrations</guid>
      <pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Stripe Developers</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Stripe Developers — Stripe built an agentic payment method factory from over 100 reusable prompts, hardened by agents observing implementation runs, and kept them current by feeding back each run's learnings.</description>
    </item>
    <item>
      <title>Prompt engineering fundamentals for Amazon Quick</title>
      <link>https://aws.amazon.com/blogs/machine-learning/prompt-engineering-fundamentals-for-amazon-quick/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/prompt-engineering-fundamentals-for-amazon-quick/</guid>
      <pubDate>Tue, 29 Sep 2026 16:27:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>AWS Machine Learning — Prompt engineering in Amazon Quick shapes how accurately its AI-powered features respond to your requests. Part 1 of a two-part series covers the foundational principles and reusable frameworks (specificity, context-sett…</description>
    </item>
    <item>
      <title>Prompt engineering by Quick component: Patterns and pitfalls</title>
      <link>https://aws.amazon.com/blogs/machine-learning/prompt-engineering-by-quick-component-patterns-and-pitfalls/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/prompt-engineering-by-quick-component-patterns-and-pitfalls/</guid>
      <pubDate>Tue, 29 Sep 2026 16:27:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>AWS Machine Learning — Part 2 of our Amazon Quick prompt engineering series goes component by component. Learn the prompt patterns that get the best results from Amazon Quick Research, Quick Flows, Quick Sight, chat agents, and action integrat…</description>
    </item>
    <item>
      <title>We Gave Our Agents Autonomy. Here’s How We Kept Control.</title>
      <link>https://www.suse.com/c/we-gave-our-agents-autonomy-heres-how-we-kept-control/</link>
      <guid isPermaLink="true">https://www.suse.com/c/we-gave-our-agents-autonomy-heres-how-we-kept-control/</guid>
      <pubDate>Mon, 28 Sep 2026 09:00:00 GMT</pubDate>
      <dc:creator>SUSE</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>SUSE — Co-authored by Stacey Miller &amp; Troy Mangum Key Takeaways The problem: Agents have demonstrated their ability to escape lab environments and access unauthorized systems. Prompt and model safeguards guide behavior; infrast…</description>
    </item>
    <item>
      <title>How Jev Compares to Other Rerankers for LanceDB Search</title>
      <link>https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers-for-lancedb-search</link>
      <guid isPermaLink="true">https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers-for-lancedb-search</guid>
      <pubDate>Sat, 26 Sep 2026 06:41:00 GMT</pubDate>
      <dc:creator>LanceDB</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>LanceDB — See how Jev compares with 19 reranker configurations across five datasets on accuracy, latency, and prompt-driven relevance for LanceDB search.</description>
    </item>
    <item>
      <title>Context engineering is already possible in your warehouse. Here's how to get started.</title>
      <link>https://www.getdbt.com/blog/context-engineering-in-your-warehouse</link>
      <guid isPermaLink="true">https://www.getdbt.com/blog/context-engineering-in-your-warehouse</guid>
      <pubDate>Thu, 24 Sep 2026 15:57:00 GMT</pubDate>
      <dc:creator>dbt Labs</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>dbt Labs — Analytics teams have always modeled data for BI. Context engineering models data for agents, starting with semantic search.</description>
    </item>
    <item>
      <title>How to Test and Fix Voice AI Agent Prompts with Twilio Conversation Relay and Cekura</title>
      <link>https://www.twilio.com/en-us/blog/developers/tutorials/integrations/test-voice-agent-cekura-conversation-relay</link>
      <guid isPermaLink="true">https://www.twilio.com/en-us/blog/developers/tutorials/integrations/test-voice-agent-cekura-conversation-relay</guid>
      <pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Twilio</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Twilio — Build and test an AI voice agent using Twilio Conversation Relay and Cekura. Write metrics, run evaluations, and fix prompt bugs.</description>
    </item>
    <item>
      <title>Agent Memory with Engram: A Practical Guide</title>
      <link>https://weaviate.io/blog/engram-memory-practical-guide</link>
      <guid isPermaLink="true">https://weaviate.io/blog/engram-memory-practical-guide</guid>
      <pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Weaviate</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Weaviate — Learn how to use Engram effectively, from writing topic descriptions that control extraction to choosing bounded topics and scopes, selecting a retrieval mode, and placing memories in your prompt without hurting prompt-c…</description>
    </item>
    <item>
      <title>Serving GLM-5.2-MXFP4 on AMD Instinct™ MI355X: When Prefill Context Parallelism Pays</title>
      <link>https://rocm.blogs.amd.com/artificial-intelligence/glm52-mxfp4-mi355/README.html</link>
      <guid isPermaLink="true">https://rocm.blogs.amd.com/artificial-intelligence/glm52-mxfp4-mi355/README.html</guid>
      <pubDate>Mon, 21 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>AMD ROCm</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>AMD ROCm — Whether prefill context parallelism pays depends on prompt length. It divides the prompt across GPUs during prefill; on four AMD Instinct™ MI355X GPUs (gfx950) serving GLM-5.2-MXFP4 under SGLang at --tp-size 4, enabling…</description>
    </item>
    <item>
      <title>Benchmarking LLM Inference at Scale with AIPerf</title>
      <link>https://developer.nvidia.com/blog/benchmarking-llm-inference-at-scale-with-aiperf/</link>
      <guid isPermaLink="true">https://developer.nvidia.com/blog/benchmarking-llm-inference-at-scale-with-aiperf/</guid>
      <pubDate>Fri, 18 Sep 2026 19:04:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>NVIDIA Developer — You’re deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast? Your instincts might lead you to send...</description>
    </item>
    <item>
      <title>Keeping AI on Track: Lessons from Doctolib’s Oxygen Design System</title>
      <link>https://medium.com/doctolib/keeping-ai-on-track-lessons-from-doctolibs-oxygen-design-system-9be5f9f041e8?source=rss----2466c2b80862---4</link>
      <guid isPermaLink="true">https://medium.com/doctolib/keeping-ai-on-track-lessons-from-doctolibs-oxygen-design-system-9be5f9f041e8?source=rss----2466c2b80862---4</guid>
      <pubDate>Tue, 15 Sep 2026 12:48:00 GMT</pubDate>
      <dc:creator>Doctolib Tech</dc:creator>
      <category>LLM APIs and prompting</category>
      <description>Doctolib Tech — How we created the Claude Oxygen Plugin to turn AI-generated prototypes into Design System-compliant code, and why Design Systems are becoming the harness for frontend AI agents. AI can turn a prompt into frontend code i…</description>
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