How to Navigate AI Resources and Tools

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

My Service Design team at LinkedIn has been actively wokring to experiment with and learn about the new AI tools. So far we've learned that: 🔹 We've got to try the tool to know the tool 🔹 We should document everything - prompts used, tools tried, use cases applied, everything 🔹 We should spend more time writing better prompts to get better results 🔹 We've got to set aside time for learning, for experimentation, for reflection It's now been over a month of intensive experimentation and testing of new AI tools for myself and my team. Some people have been experimenting far longer than that. This post is not for you (though if you have words of wisdom to share in the comments, please do!). If you haven't started trying out the new AI Tools, or you're early in your journey, read on. I wanted to share an early version of a framework for thinking about how to get a handle on the emerging AI Tools landscape: 1️⃣ Learning about new tools - prompt the LLMs, listen, read and watch There are so many new tools to potentially test out. Whatever the use case, I always start by checking with Claude about which tools it would recommend and why. But I also want serendipity and so I am listening to and watching podcasts - Dive Club, Lenny's, How I AI, Beyond the Prompt are my go-tos for now - and reading sources like Lenny's Newsletter, and the Listed AI newsletter to learn about new tools and new use cases. I'm sure there are many other great sources. ❓ What are you go-to sources for learning about AI? 2️⃣ Documenting and evaluating everything that we do to help with tool selection Not only do you have to sift through a growing mountain of new tools, but you also have to match tools with use cases. With that in mind, my team and I keep a running list of problems or opportunities we want to test out AI tools on. In addition, I'm documenting the steps in our workflow, what tools we use today, and where we might integrate new tools to save us time, or improve the quality of our work. This makes it easier to match potential tools with use cases and parts of our workflow. 💡 Clearly documenting what you do and where you might apply new AI tools makes it far easier to move past the paradox of choice with all the new tools, and select a subset to try out. 3️⃣ Set aside time to experiment with the tools - learning the tools does require an investment of time. We meet regularly as a team to experiment and test out new tools. The possibilities of productivity improvements, and quality improvements are real enough that it makes sense for us to devote time to this. I believe it will pay off significantly in the long term. ⏲️ You aren't getting more time in your week, and I'm guessing you were already busy, so you have to make a conscious choice to repurpose some existing time for testing out the new tools. ❓How are you approaching the new AI tools? #AI #Vibecoding #LIPostingDayJune

Want to future-proof your career and start leveraging AI? Here's how I did it, ranked from easiest to most ambitious: 1️⃣ 𝗥𝗲𝗮𝗱 𝘂𝗽 𝗼𝗻 𝗔𝗜 𝘁𝗿𝗲𝗻𝗱𝘀, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝘂𝘀𝗲, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗴𝗲𝘁 𝗮𝗰𝗾𝘂𝗮𝗶𝗻𝘁𝗲𝗱 • 𝗥𝗘𝗔𝗗: https://lnkd.in/eT-nzYP9 I recommend Heather Murray 's AI for Non-Techies Newsletter. It's a fun treasure trove of useful information. • 𝗥𝗘𝗦𝗣𝗢𝗡𝗦𝗜𝗕𝗟𝗘 𝗨𝗦𝗘: AI (Generative AI especially) is not infallible. Learn about the mistakes it can make, the issues it can cause, and how to navigate them. • 𝗧𝗘𝗦𝗧 (𝗜𝗻 𝗧𝗵𝗲 𝗙𝗹𝗼𝘄 𝗼𝗳 𝗪𝗼𝗿𝗸): For $15/mo, Canva is an amazing option because you can test alot of current capabilities. For $20/mo, Microsoft Copilot Pro can be added to your Office 365 account. Also for $20/mo, Google offers AI premium for your workspace (GMail, Docs, Sheets, etc). 2️⃣ 𝗔𝗽𝗽𝗹𝘆 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗶𝗻𝗴 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘀𝗸𝗶𝗹𝗹𝘀. If your company offers access to AI tools, get access and use them according to their use policy. If not, create sample scenarios at home and practice. 3️⃣ 𝗙𝗶𝗻𝗱 𝗮𝗻 𝗔𝗜 𝗺𝗲𝗻𝘁𝗼𝗿 𝘄𝗵𝗼 𝗵𝗮𝘀 𝗺𝗮𝗱𝗲 𝗮 𝘀𝗶𝗺𝗶𝗹𝗮𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻. Share that you're interested in learning more in your field. Ask if coworkers or your LinkedIn network if anyone incorporated AI into their work. Offer to continue to learn together. 4️⃣ 𝗔𝘁𝘁𝗲𝗻𝗱 𝗔𝗜 𝘄𝗲𝗯𝗶𝗻𝗮𝗿𝘀 𝗮𝗻𝗱 𝗲𝘃𝗲𝗻𝘁𝘀 𝘁𝗼 𝗼𝗽𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘆𝗲𝘀 𝘁𝗼 𝗻𝗲𝘄 𝗽𝗼𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀. There's no shortage of free webinars, conferences, etc. talking about AI. Get involved. 5️⃣ 𝗘𝗻𝗿𝗼𝗹𝗹 𝗶𝗻 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝗮𝗻𝗱 𝗴𝗲𝘁 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗶𝗻 𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗳𝗶𝗲𝗹𝗱. Professional organizations and technology vendors offer lots of free training for specific use cases. 6️⃣ 𝗝𝗼𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁. Talk to your manager about opportunities. Make it one of your professional goals to stand out. If they aren't there, contact your professional or volunteer organizations. 7️⃣ 𝗣𝗶𝘁𝗰𝗵 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝗼 𝗴𝗲𝘁 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Use what you've learned to pitch an opportunity to create value at your company, your professional, or your volunteer organizations. Do these make sense for you? How are you going about it? #artificialintelligence #innovation #changemanagement #technology #digitaltransformation

Here’s an insider’s tip for staying tapped into AI change: make an AI bake-off part of your daily routine. I pit my of-the-moment favs against each other continually, and I have to admit my love for them changes as quickly as they do. The velocity of change is staggering, and I see the models performing (in an oddly human way) differently, at times, day to day. Sometimes it is capabilities going up or strangely, down. Other times it’s clear changes in the pre-prompt instructions (a set of prompts that the model has to abide by in returning responses), for better or worse. I’ve found this is not only the best way to see changes as it happens, but to get the most value from my robot minions 😊 It adds mere seconds to add an AI bake-off to your routine. Pick your favorite 2-3 tools and keep a browser open for each. Right now, I am running every prompt through both GPT-4 and Claude 3, with a Perplexity window constantly open for questions. Companies are doing bake-offs too. While initially, the enterprise may have been experimenting with a single model or two, now many are testing multiple models so that they can be thoughtful about which use cases they channel to which model (cost vs. performance is often a factor), and to tap into changes as the whole space continues to move rapidly. >>>> What are the tools you are most excited about today? >>>> Any tips or ideas to be smart and efficient with AI bake-offs? _________ Hi 👋 I’m Alison McCauley. I’ll be diving more into the challenges and opportunities of AI change in future posts. Follow me for more on being human at the AI crossroads 🙋♂️ 🤖 💡 #ai #artificialintelligence #innovation

I curated and reviewed 16 AI strategy playbooks so you don’t have to. These strategy roadmaps offer guidance and inspiration for any tech leader driving AI, automation, or enterprise transformation. They’re practical, high-impact resources from top consulting firms, tech giants, and respected professional associations. Here’s the curated list (in alphabetical order): 1) Accenture – The Art of AI Maturity ➜ https://lnkd.in/g4kyWCNd 2) Amazon – AI/ML/GenAI Cloud Framework ➜ https://lnkd.in/gbmUAgQT 3) Bain – Transforming CX with AI ➜ https://lnkd.in/gqq-66ST 4) Bain – Winning with AI ➜ https://lnkd.in/gWk84MjS 5) Booz Allen – Securing AI ➜ https://lnkd.in/gceVreFG 6) BCG – Transforming with AI ➜ https://lnkd.in/gWtqJFuB 7) Deloitte – AI Transformation ➜ https://lnkd.in/gGNURxzq 8) Google – AI Adoption Framework ➜ https://lnkd.in/gCj2S6uF 9) IBM – CEO’s Guide to GenAI ➜ https://lnkd.in/gqDam-yS 10) McKinsey – The Executive’s AI Playbook ➜ https://lnkd.in/gFRqm2MW 11) Microsoft – CIO’s GenAI Playbook ➜ https://lnkd.in/gbJ4vwVE 12) PMI – DS/AI Project Playbook ➜ https://lnkd.in/g7_wQbRs 13) PwC – Agentic AI Playbook ➜ https://lnkd.in/gSicWfeV 14) PwC & Microsoft – Deploying AI at Scale ➜ https://lnkd.in/gwg-CBBb 15) Scaled Agile – AI-Augmented Workforce ➜ https://lnkd.in/gunGGgWJ 16) World Economic Forum – AI C‑Suite Toolkit ➜ https://lnkd.in/gh-FQT72 🔍 Three things that stood out: 1) Business-first strategy: The best firms align AI with outcomes like OKRs - not just tech stacks. 2) Track what matters: Success depends on measuring value with the right metrics, not just completing sprints and deployments. 3) Stay agile: AI evolves fast - successful organizations adapt quickly with flexible teams and tools. ♻️ If this sparked an idea, save it or pass it along. I will be sharing more on Agile & AI adoption, automation blueprints, and use cases - follow for the next drop. #Automation #Strategy #DigitalTransformation

You don't need more AI tools → You need an AI strategy. Everyone's rushing to "use AI in their business." But randomly testing tools isn't a strategy. Here's how to actually implement AI effectively 👇 First, work backwards: → What tasks consume most of your time? → Where do you need faster output? → What could be improved with automation? Then, audit your workflow: → What requires human creativity? → What's repetitive but necessary? → What needs a human final touch? Now choose your AI tools based on needs: Low-complexity tasks: → Email drafts → Social media captions → Basic research → Meeting summaries High-complexity tasks: → Content strategy → Market analysis → Customer insights → Product development Implementation approach: → Start with one process → Test and measure results → Document what works → Scale gradually Pick 2-3 use cases maximum. Master them before adding more. Remember: AI is a tool, not a solution. The key is knowing where it fits in YOUR business. Success comes from strategy first, tools second. #AIStrategy #BusinessGrowth #Productivity P.S. Want my tested AI workflows? Drop a "+" below.