How to use prompt engineering for AI in government

Hot off the heels of our Tabnine Agentic Platform launch, this The New Stack article emphasizes the importance of enterprise context for successful agentic deployment. The New Stack

🔥 Context is the difference between a helpful AI and a risky one. 🔥 This new piece from The New Stack featuring Tabnine absolutely nails it. 🎯 Enterprise AI isn’t just about 🧠 “smart models” — it’s about 🔒 secure, context-aware systems that truly understand your environment, your code, and your compliance boundaries. As someone who talks every day with engineering leaders across aerospace, defense, and finance, this hit home. The teams getting AI right aren’t chasing hype — they’re building trustworthy, context-driven systems that scale safely. Check it out! 👇

We have come a long way from LLM chatbots to RAG systems to AI agents, but still there is one challenge that persists: 🔎 context. Your AI systems should be equipped with proper context and this is where context engineering emerges as a discipline to optimally provide the right context at the right time to your AI systems. Read the full blog post 👉 https://ow.ly/3bsf50Xc16J and dive deep into the world of context engineering.

In today's AI-driven world, the ability to craft effective prompts remains a vital skill, even if "prompt engineer" isn't a standalone job title anymore. Prompt engineering helps professionals steer generative models toward smarter, more relevant outputs. As AI becomes embedded in every workflow, knowing how to ask the right question becomes a competitive edge. Stay ahead by mastering how you interact with AI, not just what tool you use. #AI #PromptEngineering #Business  

Last week we launched Context-Bench, a new leaderboard that measures how good AI models are at Agentic Context Engineering. This week, we're doubling down with a new addition: Context-Bench Skills. Context-Bench Skills measures whether or not an agent is capable of acquiring and utilizing specialized knowledge - a critical behavior pattern required to create self-improving AI. At Letta, we call this concept "context mounting" - similar to how you mount a storage volume or USB drive to a computer. When you deploy an agent into the real world, it's impossible to endow them with all the information they need to know ahead of time. Ideally, your AI should be able to continually learn, either by creating new memories, or by acquiring preexisting knowledge (also called "skills"). Of course, an AI should also be able to "shed" or "unmount" a skill once it's no longer needed to preserve valuable space in the context window. Anthropic recently released a set of open source skills to teach Claude to do various tasks, ranging from graphic design, to Slack GIF creation, to MCP server building... but they never released a quantitative evaluation showing whether or not these skills *really* helped in practice. That's where Context-Bench Skills comes in. Context-Bench Skills shows that not only do skills help agents complete complex tasks, but many frontier models (not just Claude) are quite capable at skill acquisition. As part of our evaluation, we built skills into Letta Code, a model-agnostic harness that enables any LLM to leverage skills. This means GPT-5, Gemini, GLM-4.6, and any other model can now utilize any skills library, including those made by Anthropic.

Why Agentic AI Needs a Context-Based Approach. The key to unlocking AI's true potential lies in context engineering, moving beyond vague prompts to build reliable, enterprise-aware agents.

8 Prompt Engineering Skills You Need to Fully Utilize AI Tools Read more: https://lnkd.in/dB79c5Yt #IndiaTechnologyNews #PromptEngineering #AITools #AIEducation #ArtificialIntelligence #AIDriven #AIProductivity #MachineLearning #AIGuide #AIInnovation #FutureOfWork #TechSkills #DigitalSkills

Prompt engineering, the art of optimizing inputs for AI models, has become essential for organizations seeking accuracy, speed, and cost efficiency. Yet many teams still approach it manually, tweaking prompts through trial and error until they land on acceptable results. This ad hoc process works, but it doesn’t scale. A structured, automated approach can deliver far better outcomes, and that’s exactly what leading organizations are now implementing. Read the full article to learn how teams are designing scalable, AI-driven prompt engineering processes and why automation is the key to unlocking reliable, enterprise-grade AI performance: https://lnkd.in/ewWZkpyS #PromptEngineering #AI

PromptTANK™ (patent-pending) isn’t a shortcut for writing pretty instructions. It’s the layer that makes sure your AI doesn’t hallucinate, contradict policy, or mislead a client. It’s what sits between “I typed something” and “my business can rely on this.” If you want a faster way to write prompts, great. If you want your AI to operate like a system instead of a gamble, that’s where we live. btw your money is wayyyy better spent in the prompt engineering course and we recommend anything with Jules White on Coursera

Does AI actually boost developer productivity? I just watched this interesting talk by Yegor Denisov-Blanch who quality-checked code from millions of commits from Github to figure out if using AI increases developer productivity. (One of the) conclusions: Using AI increases your productivity. It leads to more code that needs rework (e.g. it introduced bugs), but still the net gains are significant (est. 15-20%) I guess proper context engineering using tools like https://specstory.com/ will increase productivity further and allow even complex tasks to be successfully solved by AI. source: https://lnkd.in/eyZe5Ew9