This is a summary of the book titled “Win the inside game” written by Steve Magness and published by HarperOne in 2025. The author is a performance coach who argues for a cognitive and psychological strategies to start living your full potential especially when there are increasing numbers of burnouts and for a many, a crisis of meaning. As we immerse ourselves in workplace and social media, Steve suggests developing a healthy sense of self-worth and intrinsic motivation. It’s just that we are in survival mode with the pressures of the modern life that we are merciless on ourselves and what it means for as growth and purpose. Hard work is not always virtuous. Intrinsic motivation and playful exploration will foster a sense of belonging and growth. By accepting ourselves and showing self-compassion, we can embrace the messiness of life. We learn to recast failures and losses as opportunities for learning and growth. We must proactively surround ourselves with people, objects and environments that support our growth. We will find more freedom and authenticity by disrupting the state of fear.

Many people live in "survival mode," feeling trapped in a fight for survival due to the pressures of modern life. This mode, which involves avoiding or shutting down, fighting and defending, narrowing and clinging, or accepting and exploring threats, can hinder growth and undermine a sense of life's meaning. Existential psychologist Tatjana Schnell suggests four qualities essential for a meaningful life: coherence, significance, purpose, and belonging. However, these qualities can be elusive in the modern world, with social media platforms encouraging inauthentic self-presentation, productivity-obsessed work culture, and superficial online interactions failing to foster a deep sense of belonging. Recognizing and addressing these needs can help individuals thrive in a world that is too big for their minds to handle.

The belief that hard work is virtuous can hinder happiness in the modern world. The Protestant notion that hard work is a virtue has led to an unhealthy fixation on external indicators of success, leading to poorer performance, stress, anxiety, and burnout. To thrive, prioritize intrinsic motivations and do what matters to you rather than competing with others.

High performance comes from intense work and commitment, which can grow only from an internal drive that manifests at the intersection of interest, motivation, and talent. Children naturally explore various interests, often becoming obsessed for periods of time. As people grow older, they often feel a need to choose a particular path, but rigid attachment to a narrow identity can lead to feelings of missing out and a crisis of meaning.

To achieve sustainable excellence, bring childlike exploration back into your life, seek a balance between exploration and commitment, alternate between narrowing and broadening focus, and be wary of success that might lead to cementing a commitment for the wrong reasons.

To embrace the messiness of life, cultivate self-compassion, "be someone," and "integrate the messiness." Accept your inner critic and focus on wisdom and courage to alleviate suffering. Hold onto a core sense of yourself that endures even in failures and setbacks. Seek meaning from diverse sources, such as hobbies or volunteering. Craft an empowering narrative of your journey to increase resilience and stress management. Recast failures and losses as opportunities for learning and growth.

In today's world, losing well is essential. Learning to lose well means accepting a loss and learning from it, rather than throwing a tantrum or shutting down. Failure brings clarity and helps you see yourself and pursuits as they are. Learning to lose well also helps you win better, as emotional outbursts or avoidance after a loss can lead to retreat and self-protection. Reframe your performance and view success and failure as part of your learning and growth journey.

To exit survival mode, create an environment that supports growth and downregulates the nervous system. Research shows that a person's physical environment can significantly impact their performance, with studies showing that making an office feel more like home can improve performance by up to 160%. This creates psychological ownership, which supports emotional needs for identity, belonging, and safety. Surround yourself with people who inspire you and serve as role models and cultivate relationships that feel expansive.

To find more freedom and authenticity, disrupt the state of fear by using physiological techniques to reset the nervous system. If your fears aren't life-threatening, confront them deliberately, such as dressing in a ridiculous outfit and going out in public.

Reducing attachment to specific outcomes and approaching life with more openness can help you stop living in a fearful, protective, and defensive state and start thriving. By embracing change, you can grow, adapt, form genuine relationships, and achieve goals that align with your authentic self.

 This is a summary of the book titled “Energy - a human history” written by Richard Rhodes and published by Simon and Schuster in 2018. The author is a prolific and acclaimed writer who covers major innovations in energy in the last 400 years. As a journal of scientific history, this book covers breakthroughs such as turning coal into steam, building railroads, electric grid and automobiles and eventually to harness the power of atom. His lessons on the benefits and risks of each source of energy holds value as we evaluate the challenges of climate change. Cheap abundant energy has driven prosperity for society. Wood was scarce and that prompted finding coal, but mining and transportation was difficult. Canals were built and steam drove the railway beginning with freight in 1831. The search for oil began because kerosene could be distilled from bitumen. Electricity was a breakthrough for the industrial age and the invention of the internal combustion engine propelled transportation. Oil became a global hunt with Middle East becoming a huge producer. World war II spurred the development of nuclear power, and its aftermath highlighted the pollution from power generation. Understanding the benefits and risks of each source of energy is crucial to managing environmental impact.

Over the last 400 years, western societies have demonstrated remarkable innovation in finding and exploiting new energy sources. Wood gave way to coal, and coal made room for oil, as coal and oil now make way for natural gas, nuclear power, and renewables. Obscure inventors and scientists made great advances motivated by the scarcity, cost, or other shortcomings of existing energy sources, delivering more efficient sources of heat, light, and transportation.

Elizabethan England's scarcity of wood led to the search for alternatives, such as coal, which provided energy but was difficult to mine. As coal mining expanded, miners faced the problem of flooding, leading to the development of steam engines. James Watt patented a better steam engine in 1769, which was sold to coal miners and other industrialists under an exclusive patent until 1800.

Mine owners built canals to reduce the cost of transporting coal, such as the Bridgewater Canal, which reduced the price of coal in Manchester by 50%. Improved smelting allowed for the use of iron rails for efficient coal hauling.

The Liverpool and Manchester Railway, the first commercial passenger and freight railway, opened in 1831, powered by steam. Cornish inventor Richard Trevithick developed a high-pressure steam engine, allowing it to be smaller than earlier models. George Stephenson won a competition to demonstrate the safety and speed of steam-powered rail, leading to the development of the first railway in the world, the Liverpool and Manchester Railway. The first gas lights were installed in London in 1807. The search for oil began with kerosene, a fuel source for lighting, invented by Canadian physician Abraham Gesner. The US Civil War boosted the market for oil, with production reaching 4.8 million barrels by 1870. However, the environmental costs of drilling, transporting, and distilling oil became apparent, making the process messy.

Electricity was a significant energy source that fueled economic growth. Despite the existence of electricity, scientists were unsure of how to use it. Hans Christian Oersted discovered electromagnetism, which enabled the generation of electricity in sufficient quantities for practical use. Early developers recognized Niagara Falls as a potential power source and worked to harness its potential effectively. William Stanley Jr. developed alternating current (AC), allowing transmission over long distances. Westinghouse built generators and transmission lines to harness Niagara Falls' power, making Buffalo, New York, the first electrified city. The introduction of electric streetcars in the 1880s reduced transportation costs and accelerated city growth. Henry Ford developed his first automobile in 1896, using a gasoline-powered internal combustion engine.

The need for oil became international, leading to exploration in the Middle East. In 1933, Standard Oil of California signed a 60-year deal with Saudi Arabia, leading to a significant discovery in 1938. The development of oil fields required the construction of oil and gas pipelines, which were later used to deliver natural gas, a by-product of oil production. The outbreak of World War II boosted the demand for oil, leading to the construction of the world's largest and longest oil pipeline, the Big Inch. The Atomic Energy Act granted the US government a monopoly on nuclear power, but the Atomic Energy Commission created a joint venture to build a nuclear reactor near Pittsburgh in 1953. By the 1950s, the problem of pollution from power generation became evident, and the connection between air pollution and health was poorly understood until the mid-20th century.

In the 1950s, a chemist at the California Institute of Technology discovered that smog in Los Angeles was caused by automobile and factory emissions interacting with sunlight and ozone. This led to the 1970 US Clean Air Act. Wealth has been linked to environmental regulation, with wealthier societies becoming cleaner and healthier. Understanding the benefits and risks of energy sources is crucial for managing environmental impact and addressing climate change. Climate change has increased public awareness, leading to research on renewable energy sources like wind and solar power.

 The vectors generated by embedding models are often stored in a specialized vector database. Vector databases are optimized for storing and retrieving vector data efficiently. Like traditional databases, vector databases can be used to manage permissions, metadata and data integrity, ensuring secure and organized access to information. They also tend to include update mechanisms so newly added texts are indexed and ready to use quickly.

The difference that a vector database and Retrieval Augmented Generation makes might be easier to explain with an example. When a chatbot powered by LLama2 LLM is asked about an acronym that was not part of its training text, it tends to guess and respond with an incorrect expansion and elaborating on what that might be. It does not even hint that it might be making things up. This is often referred to as hallucination. But if an RAG system is setup with access to documentation that explains what the acronym stands for, the relevant information is indexed and becomes part of the vector database, and the same prompt will now give a more pertinent information. With RAG, the LLM provides correct answers.

If the prompt was provided with the relevant documents that contain an answer, which is referred to as augmenting the prompt, the LLM can leverage that against the vector database and provide more compelling and coherent answers that would turn out to be knowledgeable as well as opposed to the hallucination referred above. By automating this process, we can make the chat responses to be more satisfactory every time. This might require additional steps of building a retrieval system backed by a vector database. It might also involve extra steps of data processing and managing the generated vectors. RAG also has added benefits for the LLM to consolidate multiple sources of data into a readable output tailored to the user's prompt. RAG applications can also incorporate proprietary data which makes it different from the public data that most LLM are trained on. The data can be up to date so that the LLM is not restricted to the point-in-time that it was trained on. RAG reduces hallucinations and allows the LLM to provide citations and query statistics to make the processing more transparent to the users. As with all retrieval systems, fine-grained data access control also brings about its own advantages.

There are four steps for building Retrieval-Augmented Generation (RAG):

1. Data Augmentation

a. Objective: Prepare data for a real-time knowledge base and contextualization in LLM queries by populating a vector database.

b. Process: Integrate disparate data using connectors, transform and refine raw data streams, and create vector embeddings from unstructured data. This step ensures that the latest version of proprietary data is instantly accessible for GenAI applications.

2. Inference

a. Objective: Connect relevant information with each prompt, contextualizing user queries and ensuring GenAI applications handle responses accurately.

b. Process: Continuously update the vector store with fresh sensor data. When a user prompt comes in, enrich and contextualize it in real-time with private data and data retrieved from the vector store. Stream this information to an LLM service and pass the generated response back to the web application.

3. Workflows

a. Objective: Parse natural language, synthesize necessary information, and use reasoning agents to determine the next steps to optimize performance.

b. Process: Break down complex queries into simpler steps using reasoning agents, which interact with external tools and resources. This involves multiple calls to different systems and APIs, processed by the LLM to give a coherent response. Stream Governance ensures data quality and compliance throughout the workflow.

4. Post-Processing

a. Objective: Validate LLM outputs and enforce business logic and compliance requirements to detect hallucinations and ensure trustworthy answers.

b. Process: Use frameworks like BPML or Morphir to perform sanity checks and other safeguards on data and queries associated with domain data. Decouple post-processing from the main application to allow different teams to develop independently. Apply complex business rules in real-time to ensure accuracy and compliance and use querying for deeper logic checks.

These steps collectively ensure that RAG systems provide accurate, relevant, and trustworthy responses by leveraging real-time data and domain-specific context

#codingexercise: CodingExercise-03-31-2025.docx

 This is a summary of the book titled “Prompt Engineering for Generative AI - Future Proof inputs for reliable AI outputs” written by James Phoenix and Mike Taylor and published by O’Reilly in 2024. The authors are data scientists who explain that the varying results from queries to Large Language Models such as ChatGPT can be made more accurate, relevant and consistent with prompt engineering which focuses on how the inputs are worded so that users can truly harness the power of AI. With several examples, they teach the ins and outs of crafting text- and image-based prompts that will yield desirable outputs. LLMs, particularly those that are used in chatbots are trained on large datasets to output human like text and there are some principles to optimize the responses, namely, set clear expectations, structure your request, give specific examples, and assess the quality of responses. Specify context and experiment with different output formats to maximize the results. “LangChain” and “Autonomous agents” are two features of LLMs that can be tapped to get high quality responses. Diffusion models are effective for generating images from text. Image outputs for creative prompts can be further enhanced by training the model on specific tasks. Using the prompting principles mentioned in this book we can build an exhaustive content-writing AI.

Prompt engineering is a technique used by users to create prompts that guide AI models like ChatGPT to generate desired outputs. These prompts provide instructions in text, either to large language models (LLMs) or image-related diffusion AIs like Midjourney. Proper prompt engineering ensures valuable outputs, as generic inputs create varying outputs. Prompt engineering follows basic principles, such as providing clarity about the type of response, defining the general format, giving specific examples, and assessing the quality of responses. Large language models (LLMs) can learn from vast amounts of data, enabling the generation of coherent, context-sensitive, and human-sounding text. These models use advanced algorithms to understand the meaning in text and produce outputs that are often indistinguishable from human work. Tokens, created by Byte-pair Encoding (BPE), are used to compress linguistic units into tokens, which can be assigned numbers or vectors. LLM models are initially trained on massive amounts of data to instill a broad, flexible understanding of language, then fine-tuned to adapt to more specialized areas and tasks.

ChatGPT is a machine learning model that can generate text in various formats, such as lists, hierarchical structures, and more. To optimize its results, specify context and experiment with different output formats. To avoid issues with LLM outputs, try using alternative formats like JSON or YAML. Advanced LLMs like ChatGPT-4 can also make recommendations if the model's response is inadequate. Users can provide more context to the model to generate more accurate outputs.

LangChain, an open-source framework, can help address complex generative AI issues such as incorrect responses or hallucinations. It integrates LLMs into other applications and enables fluid interactions between models and data sources with retrieval, augmentation and generation enhancements. It allows developers to build applications like conversational agents, knowledge retrieval systems, and automated pipelines. As LLM applications grow, it's beneficial to use LangChain's prompt templates, which allow for validation, combination, and customization of prompts.

Large language models (LLMs) play a crucial role in AI evolution by addressing complex problems autonomously. They can use chain-of-thought reasoning (CoT) to break down complex problems into smaller parts, allowing for more efficient problem-solving. Agent-based architectures, where agents perceive their environment and act in pursuit of specific goals, are essential for creating useful applications. Diffusion models, such as DALL-E 3, Stable Diffusion, and Midjourney, are particularly effective in generating high-quality images from text inputs. These models are trained on massive internet data sets, allowing them to imitate most artistic styles. However, concerns about copyright infringement have been raised, but the images are not literal imitations of images or styles but are derived from patterns detected among a vast array of images. As AI image generation shifts, the focus will likely shift towards text-to-video and image-to-video generation.

AI image generation can be a creative process, with each model having its own unique idiosyncrasies. The first step is to specify the desired image format, which can be stock press photos or traditional oil paintings. AI models can replicate any known art style, but copyright issues should be considered. Midcentury allows users to reverse engineer a prompt from an image, allowing them to craft another image in the sample's style.

Stable Diffusion, an open-source image generation model, can be run for free and customized to suit specific needs. However, customization can be complicated and best done by advanced users. The web user interface AUTOMATIC1111 is particularly appealing for serious users, as it allows for higher resolution images with significant controls. DreamBooth can be used to fine-tune the model to understand unfamiliar ideas in training data.

To create an exhaustive content-writing AI, users should specify the appropriate writing tone and provide keywords. Blind prompting can make it difficult for the model to evaluate its own quality but providing at least one example can significantly improve the response quality.

 A previous article1 described how the formation of a UAV swarm flows through space and time using waypoints and trajectory. While the shape formations in these cases are known, the size depends on the number of units in the formation, the minimum distance between units, the presence of external infringements and constraints and the margin required to maintain from such constraints. An earlier prototype2, also described the ability to distribute drones to spread as close to the constraints using self-organizing maps which is essentially drawing each unit to the nearest real-world element that imposes a constraint such as when drones fly through tunnels by following the walls. This establishes the maximum boundaries for the space that the UAV swarm occupies with the core being provided by the waypoints and trajectory that each unit of the swarm can follow one after the other in sequence if the constraints are too rigid or unpredictable. The progress along the trajectory spanning the waypoints continues to be with the help the center of the formation. Given the minimum-maximum combination and the various thresholds for the factors cited, the size of the shape for the UAV swarm at a point of time can be determined.

This article argues that the vectorization, clustering and model does not just apply to the UAV swarm formation in space but also applies to maintaining a balance between constraints and sizing and determining the quality of the formation, using Vector-Search-as-a-judge. The idea is borrowed from LLM-as-a-judge3 which helps to constantly evaluate and monitor many AI applications of various LLMs used for specific domains including Retrieval Augmented Generation aka RAG based chatbots. By virtue of automated evaluation with over 80% agreement on human judgements and a simple 1 to 5 grading scale, the balance between constraints and sizing can be consistently evaluated and even enforced. It may not be at par with human grading and might require several auto-evaluation samples, but these can be conducted virtually without any actual flights of UAV swarms. A good choice of hyperparameters is sufficient to ensure reproducibility, single-answer grading, and reasoning about the grading process. Emitting the metrics for correctness, comprehensiveness and readability is sufficient in this regard. The overall workflow for this judge is also like the self-organizing map in terms of data preparation, indexing relevant data, and information retrieval.

As with all AI models, it is important to ensure AI safety and security4 to include a diverse set of data and to leverage the proper separation of the read-write and read-only accesses needed between the model and the judge. Use of a feedback loop to emit the gradings as telemetry and its inclusion into the feedback loop for the model when deciding on the formation shape and size, albeit optional, can ensure the parameters of remaining under the constraints imposed is always met.

The shape and size of the UAV formation is deterministic at a point of time but how it changes over time depends on the selection of waypoints between source and destination as well as the duration permitted for the swarm to move collectively or stream through and regroup at the waypoint. A smooth trajectory was formed between the waypoints and each unit could adhere to the trajectory by tolerating formation variations.

Perhaps, the biggest contribution of the vectorization of all constraints in a landscape is that a selection of waypoints offering the least resistance for the UAV swarm to keep its shape and size to pass through can be determined by an inverse metric to the one that was used for self-organizing maps.

#Codingexercise

https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNaVNQEAAAAB63QJqDjFIKM2Vwrg34NWVQ?e=grnBgD

 Measuring RAG performance:

Since a RAG Application has many aspects that affect its retrieval or generation quality, there must be ways to measure its performance, but this is still one of the most challenging parts of setting up a RAG Application. It sometimes helpful to evaluate each step of the RAG Application creation process independently. Both the model and the knowledge base must be effective.

The evaluations in retrieval step, for instance, involves identifying the relevant records that should be retrieved to address each prompt. A precision and recall metric such as F-score can come helpful in benchmarking and improvements. Generating good answers to those prompts can also be evaluated so that it is free of hallucinations and incorrect responses. Leveraging another LLM to provide prompts and to check responses can also be helpful and this technique is known as LLM-as-a-judge. The scores resulting from this technique must be simple and, in the range, say 1-5 with a higher rating indicating a true response to the context.

RAG isn’t the only approach to customizing to equipping models with new information, but any approach will involve trade-offs between cost, complexity and expressive power. Cost comes from inventory and bill of materials. Complexity means technical difficulty that is usually reflected in time, effort, and expertise required. Expressiveness refers to the model’s ability to generate diverse, inclusive, meaningful and useful responses to prompts.

Besides RAG, prompt engineering offers an alternative to guide a model’s outputs towards a desired result. Large and highly capable models are often required to understand and follow complex prompts and entail serving costs or per-token costs. This is especially useful when public data is sufficient and there is no need for proprietary or recent knowledge.

Improving overall performance also requires the model to be fine-tuned. This has a special meaning in the context of large language models where it refers to taking a pretrained model and adapting it to a new task or domain by adjusting some or all of its weights on new data. This is a necessary step for building a chatbot on say medical texts.

While RAG infuses data into the overall process, it does not change the model. Fine-tuning can change a model’s behavior, so that it need not be the same as when it was originally. It is also not a straightforward process and may not be as reliable as RAG in generating relevant responses

 Measuring RAG performance:

Since a RAG Application has many aspects that affect its retrieval or generation quality, there must be ways to measure its performance, but this is still one of the most challenging parts of setting up a RAG Application. It sometimes helpful to evaluate each step of the RAG Application creation process independently. Both the model and the knowledge base must be effective.

The evaluations in retrieval step, for instance, involves identifying the relevant records that should be retrieved to address each prompt. A precision and recall metric such as F-score can come helpful in benchmarking and improvements. Generating good answers to those prompts can also be evaluated so that it is free of hallucinations and incorrect responses. Leveraging another LLM to provide prompts and to check responses can also be helpful and this technique is known as LLM-as-a-judge. The scores resulting from this technique must be simple and, in the range, say 1-5 with a higher rating indicating a true response to the context.

RAG isn’t the only approach to customizing to equipping models with new information, but any approach will involve trade-offs between cost, complexity and expressive power. Cost comes from inventory and bill of materials. Complexity means technical difficulty that is usually reflected in time, effort, and expertise required. Expressiveness refers to the model’s ability to generate diverse, inclusive, meaningful and useful responses to prompts.

Besides RAG, prompt engineering offers an alternative to guide a model’s outputs towards a desired result. Large and highly capable models are often required to understand and follow complex prompts and entail serving costs or per-token costs. This is especially useful when public data is sufficient and there is no need for proprietary or recent knowledge.

Improving overall performance also requires the model to be fine-tuned. This has a special meaning in the context of large language models where it refers to taking a pretrained model and adapting it to a new task or domain by adjusting some or all of its weights on new data. This is a necessary step for building a chatbot on say medical texts.

While RAG infuses data into the overall process, it does not change the model. Fine-tuning can change a model’s behavior, so that it need not be the same as when it was originally. It is also not a straightforward process and may not be as reliable as RAG in generating relevant responses.

#codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EYKwhcLpZ3tAs0h6tU_RYxwBxeAeg1Vg2DH7deOt-niRhw?e=qbXLag