Propaya的动态

?? Building Smarter Tools with AI: My Latest Project I recently developed an application that takes a PDF file, processes it, and uses an LLM (LLaMA) to answer questions from the document. Diving into this project was fascinating as it required implementing key processes like: - Retrieval: Finding the right information efficiently. - Vector Storage: Storing data in a format optimized for similarity searches. - Extraction: Pulling out specific, actionable data from unstructured content. - Embedding: Converting text into numerical representations for AI understanding. - Chunking: Breaking down large documents into manageable pieces for processing. Each of these steps showcases how modern AI can handle large volumes of information, making sense of it with remarkable accuracy. But what excites me most is how these processes can transform small business automation: ? Knowledge Base Automation: Answer customer queries instantly from a product manual or FAQ document. ? Contract Analysis: Quickly extract key terms and insights from legal or vendor documents. ? Content Organization: Automatically categorize, summarize, and retrieve relevant data from a stack of reports. This project was a reminder of how powerful AI is in simplifying complex workflows and unlocking productivity. What other areas do you think AI-powered document processing could revolutionize? Let’s discuss!

AI creates actual business value in three core ways: optimization, automation, and knowledge management. In the last few months our team was asked to support a €1.3B revenue manufacturing company in their digital journey. Their traditional efforts have started to show diminishing returns, and they believe Data & AI could be the next frontier to outperform competition. Part of our mission was to answer a big question: ?????? ???????? ?????? ???????????????????????? ???????? 5-10 ?????????? ???????? ??????????, ?????????? ?????????????? ???? ???????? & ????? We started looking for game-changing opportunities across the company's value chain, using the 3 value levers of AI: 1?? Optimization: Which decisions have a large optimization space? (Tip: look for decisions that if asking 5 subject matter experts to make that decision, at least 3 different answers will come out) Example: pricing strategies or purchase planning. 2?? Automation: Which processes are human heavy, but the humans aren't adding that much unique value? Automate using AI where tasks are repetitive, data is unstructured, or personalization is key. Example: creating personalized cold lead emails or filling regulatory documents. A good way to find these processes is by asking "what types of work your people hate doing?" 3?? Knowledge Management: Which decisions depend on unstructured data as well as human involvement? AI can bridge support the experts with document retrieval and contextual answers. Example: answering technical questions or handling protocol-heavy problem-solving. By mapping the company’s value chain through these lenses, we were able to identify potential EBITDA uplift of €200M+(!!) using Data & AI.

Automated prompt engineering (APE) is a powerful approach to managing the complex and time-consuming process of manually creating prompts for AI models. Manual prompt engineering is labor-intensive, requiring deep expertise and iterative refinement, often leading to inconsistent results. As AI applications scale across industries, this process becomes increasingly inefficient, especially when deploying multiple agents for specialized tasks like financial analysis or customer service. APE solves these issues by allowing an AI agent to generate tailored prompts for other agents automatically. For example, a main agent can be instructed to create a system prompt for a financial agent analyzing quarterly earnings reports. The generated prompt would include specific steps such as providing context, outlining tasks, defining output formats, and setting constraints—ensuring precision and efficiency. The benefits of APE include saving time, enhancing scalability, and improving prompt accuracy. It enables businesses to deploy and manage thousands of agents simultaneously, each equipped with optimized, task-specific prompts, all without manual intervention. This innovation transforms AI workflows, making them more intelligent and adaptable, leading to greater efficiency across industries like finance, marketing, and customer service. Like, repost, and follow for more tips and tricks on prompts, agents, and more!

Automated prompt engineering (APE) is a powerful approach to managing the complex and time-consuming process of manually creating prompts for AI models. Manual prompt engineering is labor-intensive, requiring deep expertise and iterative refinement, often leading to inconsistent results. As AI applications scale across industries, this process becomes increasingly inefficient, especially when deploying multiple agents for specialized tasks like financial analysis or customer service. APE solves these issues by allowing an AI agent to generate tailored prompts for other agents automatically. For example, a main agent can be instructed to create a system prompt for a financial agent analyzing quarterly earnings reports. The generated prompt would include specific steps such as providing context, outlining tasks, defining output formats, and setting constraints—ensuring precision and efficiency. The benefits of APE include saving time, enhancing scalability, and improving prompt accuracy. It enables businesses to deploy and manage thousands of agents simultaneously, each equipped with optimized, task-specific prompts, all without manual intervention. This innovation transforms AI workflows, making them more intelligent and adaptable, leading to greater efficiency across industries like finance, marketing, and customer service. Like, repost, and follow for more tips and tricks on prompts, agents, and more!

A few of the AI projects we're working on. These may not be sexy, but they are practical, real, and focused on ROI. If you're a tech consultant, do this type of work. That's where the money is. If you're a business looking to solve real problems with AI, give me a shout. In either case, Contextual.io can help. 1) Analyzing digital documents (classically OCR - optical character recognition) to extract key information. This is a semi-established practice, but error rates have remained high. We focus on delivering multi-tool AI systems that 'check their work' in order to eliminate error rates. Did you see that as a ZERO or an O? Did that price include a line item for tax or not? Does this need a second review? Should it be escalated? 2) Predicting customer revenue trends to focus resources on growth or loss prevention. GenAI isn't great at time-series forecasting (or at least traditional systems are just as good), but it is great at identifying unique patterns that make things 'alike' and therefore well structured for a forecast that can be trusted. 3) Generating personalized proposals from massive product and pricing data sets. This isn't about generating lots of text in a proposal. Any LLM can do that. It's about creating the AI-friendly data sources, pulling in external information and creating proposals that speak deeply to a target's industry, challenges, and current status.

Automated prompt engineering (APE) is a powerful approach to managing the complex and time-consuming process of manually creating prompts for AI models. Manual prompt engineering is labor-intensive, requiring deep expertise and iterative refinement, often leading to inconsistent results. As AI applications scale across industries, this process becomes increasingly inefficient, especially when deploying multiple agents for specialized tasks like financial analysis or customer service. APE solves these issues by allowing an AI agent to generate tailored prompts for other agents automatically. For example, a main agent can be instructed to create a system prompt for a financial agent analyzing quarterly earnings reports. The generated prompt would include specific steps such as providing context, outlining tasks, defining output formats, and setting constraints—ensuring precision and efficiency. The benefits of APE include saving time, enhancing scalability, and improving prompt accuracy. It enables businesses to deploy and manage thousands of agents simultaneously, each equipped with optimized, task-specific prompts, all without manual intervention. This innovation transforms AI workflows, making them more intelligent and adaptable, leading to greater efficiency across industries like finance, marketing, and customer service. Like, repost, and follow for more tips and tricks on prompts, agents, and more!

The Six Design Principles of Industrial AI Agents: 1.?Modularity Agents encapsulate core functions - perception, reasoning, decision-making, and action - within a single, cohesive unit. Unlike microservices or Packaged Business Capabilities (PBCs), this modularity enables agents to function independently, reducing latency and dependency on external services. The all-in-one structure allows agents to adapt quickly and operate effectively in environments where fast response time and resilience are essential, such as real-time industrial applications and edge computing. 2.?Autonomy Autonomy allows agents to interpret data, make decisions, and act independently. This autonomy is achieved through embedded rule sets, local decision-making models, and built-in safety constraints. Together, these elements enable agents to handle complex tasks independently, improving efficiency and reducing the need for constant supervision. 3.?Reactivity and Proactivity:? Agents combine reactivity with proactivity, responding to real-time events while anticipating needs based on predefined objectives. Reactivity enables agents to address immediate issues, while proactivity drives them to take proactive actions aligned with their goals. This combined approach is especially effective in applications like predictive maintenance, where agents not only respond to alerts but also take actions to prevent potential disruptions. 4.?Adaptability Agents are designed to learn and adapt based on feedback from their environment, such as reinforcement learning. This adaptability allows agents to improve over time by refining their actions based on past outcomes, making them more effective in dynamic or evolving settings. By continuous self-optimizing, agents can maintain high performance and require minimal reprogramming, which is valuable in fast-changing industries. 5.??Goal-Orientation: Clear, predefined goals direct an agent’s actions, ensuring that all decisions align with specific objectives, such as maximizing efficiency or reducing downtime. This goal-orientation helps agents prioritize their tasks and enables them to operate proactively, making decisions that directly contribute to organizational targets. Goal-oriented agents are thus highly effective for applications where focused, outcome-driven actions are necessary. 6.?Collaboration In multi-agent systems, agents are designed to coordinate and share information through structured communication protocols. This collaboration enables agents to distribute tasks, understand roles, and achieve shared objectives more effectively. Such coordination is crucial in applications like supply chains or distributed manufacturing, where agents work together to handle interdependent tasks, optimizing the overall system performance. In Summary, these six principles make agents highly capable in complex, real-time settings, driving meaningful improvements in performance, responsiveness, and operational efficiency across industries.

Automated prompt engineering (APE) is a powerful approach to managing the complex and time-consuming process of manually creating prompts for AI models. Manual prompt engineering is labor-intensive, requiring deep expertise and iterative refinement, often leading to inconsistent results. As AI applications scale across industries, this process becomes increasingly inefficient, especially when deploying multiple agents for specialized tasks like financial analysis or customer service. APE solves these issues by allowing an AI agent to generate tailored prompts for other agents automatically. For example, a main agent can be instructed to create a system prompt for a financial agent analyzing quarterly earnings reports. The generated prompt would include specific steps such as providing context, outlining tasks, defining output formats, and setting constraints—ensuring precision and efficiency. The benefits of APE include saving time, enhancing scalability, and improving prompt accuracy. It enables businesses to deploy and manage thousands of agents simultaneously, each equipped with optimized, task-specific prompts, all without manual intervention. This innovation transforms AI workflows, making them more intelligent and adaptable, leading to greater efficiency across industries like finance, marketing, and customer service. Like, repost, and follow for more tips and tricks on prompts, agents, and more!

Infersoft builds AI agents capable of reasoning and processing complex tasks in the energy sector. Existing solutions focus on storage and static dashboards. But dashboards don’t streamline operations—automation does. Infersoft’s intelligent agents understand, interpret, and act on complex legal and operational documents, reducing errors and saving valuable time. Globally, mismanaged data and delays in decision-making cost energy companies billions every year. The stakes are even higher for documents like Joint Operating Agreements, where mistakes can directly impact well performance and revenue. Infersoft’s AI Agent models have access to and make sense of the structured and unstructured data that drives well operations, including scanned leases, contracts, and title documents. With a simplified interface, land administrators, title attorneys, and operators can: - Extract and operationalize key terms from any legacy or scanned document - Build workflows that streamline document-heavy processes - Automate cognitive tasks such as title chaining Early results have shown that Infersoft reduces document turnaround times by 90%, mitigates legal risks, and boosts productivity across teams. Founders Whit Blodgett and Hugo Reinhardt bring a unique blend of expertise to this mission. Whit has over a decade of experience building AI products, having worked with leading organizations like IBM Watson and Landing AI. As an industry leader in AI, he is constantly pushing the boundaries of what AI can achieve. Hugo complements this with hands-on expertise in well operation, backed by nearly a decade of experience in the energy sector, including roles at Ranger Energy Services, Patriot Well Solutions, and Pioneer Energy Services. Together, they are revolutionizing document management in the energy industry.

Tool calling in LangChain allows AI agents to interact with external systems like APIs, databases, or search engines to perform tasks or retrieve information, enhancing their functionality. In **RAG (Retrieval-Augmented Generation)**, it enables agents to fetch relevant data from sources like vector databases (e.g., Pinecone, FAISS) and use it to generate informed responses. These tools are integrated into agents, which dynamically decide when and how to call them during a workflow. For instance, an agent may use a retriever tool to pull documents based on embeddings, combine the results with its reasoning, and produce accurate answers. This process empowers AI to go beyond static responses, leveraging real-time data and multi-tool workflows for versatile, autonomous problem-solving.