Anish S’ Post

Excited to present my latest blog article, 'A Developer’s Guide to Effective Prompt Engineering: Harnessing AI Power.' I encourage you to read and share your thoughts.

🚀 Domain Experts & Domain-Trained Models are Maximizing AI Potential  🚀 When generative AI models are trained or augmented with domain-specific data, domain experts become the key to unlocking AI’s full potential. Their deep knowledge enables them to craft precise prompts, driving accurate and valuable outcomes. The Data Context Hub (DCH) platform is essential in establishing this robust data layer, empowering experts to maximize AI’s effectiveness in their fields. Learn more in this https://lnkd.in/dEdYZ6Ep #AI #PromptEngineering #DomainExpertise #DataContextHub #DigitalTransformation

🚀 **Unlocking the Power of Prompt Engineering** 🚀 In the ever-evolving landscape of Generative AI, mastering prompt engineering is crucial for leveraging these tools effectively. Our latest article outlines key insights into crafting effective prompts that drive desired outcomes while ensuring ethical AI usage. Here are some highlights: 1. **Clear Instructions**: Specific and context-aware prompts are vital. 2. **Iterative Approach**: Start simple and build complexity through feedback. 3. **Utility Prompts**: Enhance dialogue with specialized prompts. 4. **Ethical Considerations**: Prioritize responsible AI use to prevent bias. For professionals looking to enhance their AI interactions, adopting these best practices is key to unlocking the full potential of GenAI tools. Read more about it in our comprehensive guide. #PromptEngineering #GenerativeAI #ArtificialIntelligence Original Article Link: https://lnkd.in/e8BHF5Wd

Looks like the robots are learning from us; if only they could pick up our coffee habits! Summary: - Prompt engineering is vital for optimizing AI interactions and usefulness. - Success in prompt engineering requires understanding AI capabilities and iterating on input phrasing. - Stakeholders must stay informed on best practices to maximize AI benefits. #Prompt_Engineering #AI_Applications #Stakeholder_Engagement #AI_Optimization #TPMC https://lnkd.in/gv9MfyGx Author: Deven Panchal, AT&T Labs

Leveraging the full potential of #AI requires mastering the #art of #prompt engineering. This revolutionary field is like a secret code that unlocks the true power of AI models. Our blog dives deep into everything you need to know about prompt engineering, including: - Top Tools for Crafting Powerful Prompts - Unleashing AI's #Creativity & #Innovation - Prompt Engineering Use Cases Stop settling for average AI results. Learn how to prompt AI to deliver exactly what you need. Click the link below to become a prompt engineering pro! Read More: https://lnkd.in/gWeghsbj #promptengineering #artificialintelligence #Aitools #AIModels #chatgpt #openAI

Retrieval Augmented Generation (’RAG’) is one of the most popular use cases for Generative AI today. There’s a massive problem not getting talked about enough though, and it has to do with the files that you share with the AI. To better understand the problem, let’s recap how RAG works: you curate a collection of documents that you want the AI to reference and have it broken into logical chunks of texts, typically organized by context. Then, when you ask the AI a question, it cites your uploaded files in its response. While the benefits to sharing your work documents with the AI are fairly clear, the downside is that there is a trove of sensitive data in these documents that you would not want in your GenAI system. Information like: - Emails, chats, draft PR releases around pending M&A activity including filenames, deal terms, and more - Conversations with legal about pending processes, recalls - Intra-management conversations about confidential business decisions like strategy shifts or RIFs Adding to this is the fact that if you have such a system set up, there are people internally (in MLOps, Data Science) who triage issues like file upload errors and processing issues: any information in these files - unless they are screened out before even getting uploaded - is at risk of accidental exposure to unauthorized insiders. So what do you do in this situation? Enter DataFog. Our Python SDK (pip install datafog) can be loaded into different setups, ranging from an ad-hoc privacy filter that you set up in a Google Colab notebook to running a Docker container with a PII Scan workflow. Interested in learning more? Check out our sample notebook (https://lnkd.in/gqMn7VZz) to get started! Sid

🔍 Game-Changing AI Developments You Need to Know🌐 This year, AI is taking giant leaps forward, transforming how we live and work. Here’s a snapshot of the most exciting advancements: 1. 𝗠𝗶𝘀𝘁𝗿𝗮𝗹'𝘀 𝗡𝗲𝘄 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝗔𝗣𝗜:   - Mistral 7B and Mistral Small Models: Fine-tuning features for customizing AI models efficiently, making them more accessible and cost-effective.(https://www.mistral.ai). 2. 𝗔𝘅𝗲𝗹𝗲𝗿𝗮'𝘀 𝗔𝗳𝗳𝗼𝗿𝗱𝗮𝗯𝗹𝗲 𝗔𝗜 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲:   - High-Performance AI Acceleration: Axelera offers powerful AI hardware that doesn’t break the bank. (https://www.axelera.ai). 3. 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗼𝗼𝗹:   - Automated Code Enhancement: Simplifies Python code optimization, boosting productivity for developers. 4. 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆'𝘀 𝗧𝗲𝘅𝘁-𝘁𝗼-𝗔𝘂𝗱𝗶𝗼 𝗠𝗼𝗱𝗲𝗹:   - Innovative Open Weight Model: Converts text to audio, opening new possibilities for AI applications. 5. 𝗡𝗼𝗺𝗶𝗰'𝘀 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗧𝗼𝗼𝗹:   - Nomic-Embed-Vision: A cutting-edge tool for embedding visual and textual data seamlessly. 6. Top Highlights on HuggingFace:   - 𝗠𝗶𝗻𝗶𝗖𝗣𝗠-𝗟𝗹𝗮𝗺𝗮𝟯-𝗩-𝟮_𝟱: Multimodal LLMs for mobile devices.   - 𝗠𝗼𝗯𝗶𝘂𝘀: Generates high-quality, unbiased images efficiently.   - 𝗥𝗠𝗕𝗚-𝟭.𝟰: Precise image background removal.   - 𝗳𝗶𝗻𝗲𝘄𝗲𝗯-𝗲𝗱𝘂: 1.3 trillion educational tokens dataset.   - 𝗵𝘂𝗺𝗮𝗻𝗲𝘃𝗮𝗹𝗽𝗮𝗰𝗸: HumanEval extended to six languages with 4TB of commits.   - 𝗶𝗺𝗮𝗴𝗲𝗶𝗻𝘄𝗼𝗿𝗱𝘀: Detailed image descriptions with 1,612 examples. 7. 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:   - Deeplearning.ai’s AI Agentic Course: Master LangGraph for multi-agent LLMs. Stay ahead by subscribing to my weekly newsletter for the latest in AI and tech trends!

This post by Nicole C. in GitHub Blog delves into the importance of Retrieval-Augmented Generation (RAG) as a critical development in AI, offering updated information and knowledge for AI models. Companies favor RAG for its efficiency in integrating proprietary data without the need for extensive custom model training. Unlike fine-tuning, which adjusts a model's weights, RAG enhances responses by retrieving information from various sources. Context is essential for AI models to provide relevant responses, influenced by the data available within the model's context window. RAG allows AI models to extend beyond their training data, using additional information from various sources for more accurate outputs. RAG employs semantic search to improve data retrieval, enhancing the relevance and context of the information used. RAG utilizes multiple data sources, including vector databases and search engines, to fetch pertinent information for response generation. RAG is vital for customizing AI models to ensure they are informed with the latest data and organizational knowledge. #RAG #AI #ContextualUnderstanding #SemanticSearch #DataSources #Innovation #ModelCustomization #Efficiency #Flexibility

One takeaway from my conversations with users is that the way we all think about Personally Identifiable Information ('PII') needs a radical overhaul. PII Redaction is no longer just about risk mitigation in service of staying compliant with GDPR/CCPA/HIPAA/etc. As companies take more steps towards unlocking the insights of their data, what will be important is putting sensible controls to prevent sensitive information from getting exposed to internal users. It's not just a set of static end-user fields to screen against: this is a dynamic, constantly-evolving threat that companies need to manage more proactively going forward.

Explore how Program-of-Thoughts Prompting (PoTh) enhances AI's problem-solving abilities through code generation and execution, featuring detailed examples and use cases.