Tagged "prompt-engineering"
29 articles tagged prompt-engineering, 18 February 2026 to 16 September 2026. Newest first.
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Migrating Large Prompts from Anthropic to Self-Hosted Ollama
Developer shares practical lessons learned migrating 35KB preprompts from Claude Opus to self-hosted Ollama, documenting gotchas and workarounds for local LLM deployment.
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How to get better results from local LLMs with Ollama
InfoWorld covers practical strategies for optimizing inference quality and performance when running LLMs locally through Ollama, the popular self-hosted inference framework.
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Sol-5.6 and Opus 5 Models Demonstrate Strong One-Shot Game Performance
Social media discussions highlight Sol-5.6 and Opus 5's capability to solve single-example game tasks, suggesting improved reasoning and contextual understanding in local deployable models.
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Claude Code Cut System Prompt by 80%: Implications for Small Local Models
Anthropic's dramatic 80% system prompt reduction in Claude Code raises questions about prompt efficiency for smaller, resource-constrained models deployed locally.
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ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline
A GitHub project demonstrating how to construct synthetic languages using chained LLM inference, showcasing advanced prompt engineering and multi-step reasoning techniques applicable to complex local LLM workflows.
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GLM-5.2's Code Reviews Are Only as Good as Your Prompt
Analysis of code review capabilities in GLM-5.2 (a smaller local-deployable model) showing that output quality is heavily dependent on prompt engineering. Provides practical guidance for maximising local model utility.
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Good LLM Development and Usage Patterns
A practical guide outlining recommended patterns for developing and deploying LLMs in production environments, covering best practices for local and self-hosted inference.
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Three Flavors of Coding with AI Agents
An analysis of different approaches to using AI agents for code generation and development, exploring various paradigms for integrating LLMs into development workflows.
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How I Used a Local LLM to Organize the Store on My NAS
A practical guide demonstrating how to deploy a local LLM on network-attached storage hardware to automate file organization and metadata management tasks.
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LLM Hallucinations in the Wild
A comprehensive study documents real-world hallucination behaviors in deployed language models, providing practitioners with empirical data on failure modes when running models locally.
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I Think I Figured Out What an AI IDE Looks Like
A detailed exploration of IDE design patterns optimized for AI-assisted development, with implications for building integrated local LLM workflows.
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Improving Code Quality with Local Claude and Codex Models
Technical discussion on optimizing code generation quality when running Claude and Codex models locally, covering quantization, prompt engineering, and inference parameters. Practitioners share techniques for maximizing coding task performance on consumer hardware.
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Self-Hosted LLMs in Production: Real-World Limits and Practical Lessons
Deep dive into the operational challenges and workarounds for deploying LLMs in production environments, drawing on practical experience with self-hosted systems.
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Using a Local LLM as a Zero-Shot Classifier
Detailed guide demonstrating how to leverage locally-running language models for zero-shot text classification tasks without fine-tuning, reducing infrastructure costs and inference latency.
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16 Ways to Make a Small Language Model Think Bigger
Oracle has published a comprehensive guide on techniques to enhance the effective capability of small language models through prompting, retrieval, and architectural approaches—highly relevant for practitioners optimizing local deployments.
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Claude vs Local LLM: Real-World Prompt Comparison Reveals Trade-offs
A practitioner compares Claude's capabilities directly against local LLM alternatives on identical prompts, documenting performance trade-offs relevant to deployment decisions.
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Gemma 4 Template Improvements Enhance Tool Use and Dialog Compliance
An update to Gemma 4's Jinja templates improves tool calling and dialog compliance, requiring users to update their local model configurations for better results.
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Reverse-Engineering the Apollo 11 Code with AI
Researchers use AI systems to reverse-engineer and understand the Apollo 11 codebase, demonstrating practical applications of local LLMs in code analysis and historical software preservation.
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Show HN: Open Agent Spec – Treat AI Agents Like Typed Functions, Not Prompt Chains
A new specification enables developers to define AI agents with strong typing and structured interfaces, moving beyond unstructured prompt chaining for more reliable local deployments.
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Qwen 3.5 Models: Optimal Settings and Reduced Overthinking Configuration
Community exploration of Qwen 3.5 (35B and 27B) model settings and prompts reveals configurations that minimize overthinking behavior and excessive reasoning token usage. These practical optimizations help practitioners maximize output quality and inference speed.
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Self-Hosted AI Code Review with Local LLMs: Secure Automation Guide
Tutorial on implementing secure, on-device AI-powered code review using local LLMs, enabling organizations to automate code quality checks while maintaining code privacy and avoiding cloud dependencies.
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You're Using Your Local LLM Wrong If You're Prompting It Like a Cloud LLM
A practical guide highlighting how local LLM prompting strategies differ from cloud-based models, offering insights into optimizing inference for self-hosted deployments. This addresses a critical gap where many practitioners apply cloud LLM techniques to local models without accounting for architectural differences.
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Practical Fix for Qwen 3.5 Overthinking in llama.cpp
Community members share techniques to mitigate Qwen 3.5's verbose internal reasoning loops, offering practical optimization strategies for controlling model behavior in local inference environments.
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Show HN: TLDR – Free Chrome Extension for AI-Powered Article Summarization
A new Chrome extension uses AI to generate two-second summaries of any article. The project demonstrates feasibility of running inference efficiently enough for real-time browser integration.
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Qwen 3.5-27B Demonstrates Exceptional Performance with Thoughtful Prompt Engineering
Users report that Qwen 3.5-27B significantly exceeds expected performance for its size when paired with effective prompting strategies, suggesting prompt engineering can bridge the capability gap between model sizes.
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Every agent framework has the same bug – prompt decay. Here's a fix
A critical analysis identifies prompt decay as a common vulnerability in agent frameworks, where model outputs gradually degrade over extended interactions. A practical fix is proposed and shared.
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TemplateFlow – Build AI Workflows, Not Prompts
TemplateFlow introduces a workflow-based approach to local LLM deployment, moving beyond simple prompt engineering to structured, reproducible AI pipelines. This framework simplifies complex multi-step inference tasks.
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Why AI Models Fail at Iterative Reasoning and What Could Fix It
An analysis of fundamental limitations in how local LLMs perform iterative reasoning tasks and proposes solutions applicable to on-device inference and self-hosted deployments.
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Ask HN: How Do You Debug Multi-Step AI Workflows When the Output Is Wrong?
A community discussion on debugging strategies for complex multi-step AI workflows running locally, covering techniques for identifying failures and improving inference reliability.