Neural & Narrative

Tag: chatbots

  • Building Faithly: The Technical Reality Behind My Spiritual AI (Part 2 of 4)

    Building Faithly: The Technical Reality Behind My Spiritual AI (Part 2 of 4)

    When I set out to build Faithly, my vision was ambitious: create a chatbot that could support people in their spiritual life—offering scripture, encouragement, and interpretation rooted in Christian tradition. The journey from idea to MVP wasn’t smooth, but every technical challenge pushed me closer to something real, functional, and surprisingly powerful.

    Here’s the honest breakdown of what it actually took to build a spiritual AI from scratch.

    My Tech Stack (Or: How I Made Simple Things Complicated)

    Looking back, my tool choices tell the story of someone who wanted to learn everything the hard way:

    Python became my backbone for all the backend logic and scripting. It felt like the right choice for AI work, and honestly, it was one of the few languages I felt remotely confident in.

    OpenAI’s API powered the intelligence, specifically their text-embedding-3-small model for generating vector embeddings of Bible verses. This was where the real magic happened—turning ancient text into mathematical representations that could be searched and compared.

    ChromaDB served as my lightweight, local vector database for fast retrieval and search. I chose it because it seemed simpler than alternatives like Pinecone or Weaviate, though “simpler” is relative when you’re learning vector databases from scratch.

    JSON became my data format of choice for processing Bible verses with metadata (book, chapter, verse). Clean, structured, and easy to work with—when it wasn’t breaking my scripts with encoding issues.

    DigitalOcean VPS hosted everything in a virtual Python environment. This was probably overkill, but I wanted to understand the infrastructure from the ground up.

    Ghost (third-party managed) eventually became my solution for the public-facing Faithly blog and downloadable resources. More on why “eventually” in a moment.

    Canva handled the design work for Bible study templates and digital goods. Sometimes the best technical solution is admitting you’re not a designer.

    The Strategies That Actually Worked

    Through trial and error (mostly error), I developed some approaches that kept the project moving forward:

    MVP First, Features Later was my mantra. I focused solely on core functionality: embedding scripture and retrieving it based on user queries. No fancy UI, no advanced features—just the essential engine that could match user questions to relevant verses.

    Batch Processing for Embedding became essential when I hit the wall of API quotas and RAM limits. Processing 10 verses at a time kept me within OpenAI’s rate limits and prevented my 454MB RAM VPS from crashing.

    Resume from Failures saved my sanity. When my script inevitably crashed midway through batch 421 (yes, I counted), I added start_index logic to resume exactly where it left off without reprocessing thousands of verses.

    Prompt Engineering for RAG was where I spent way too much time experimenting. Getting the right format for scripture plus metadata to produce relevant results from OpenAI’s completion model was part art, part science, and part stubborn persistence.

    Ghost for Simplicity was my eventual surrender to pragmatism. After banging my head against manual server setups, I pivoted to a $6/month hosted Ghost blog. Sometimes the best technical decision is knowing when to stop being technical.

    The Technical Challenges That Humbled Me

    Every ambitious project has its reality checks. Here were mine:

    Database Nightmares started early. My attempts to self-host Ghost on DigitalOcean turned into a comedy of database connection errors. “Access denied for user ‘ghost’@‘localhost’” became my nemesis. I eventually scrapped the entire droplet and started over, which taught me the value of managed services.

    API Quotas and RAM Limits created a perfect storm of constraints. OpenAI’s API limits meant I couldn’t just fire off requests as fast as I wanted, and my VPS’s 454MB RAM made it impossible to process the entire Bible in one go. This forced me to build a custom batch/resume system that actually made the whole process more robust.

    Classic Python Pitfalls humbled me regularly. Unterminated string literals, malformed if __name__ == "__main__" blocks, encoding issues with biblical text—I hit every rookie mistake in the book. Each error taught me more about Python than I wanted to learn, but the debugging skills proved invaluable.

    ChromaDB Persistence was trickier than expected. Making sure my vector storage survived server reboots required some trial-and-error and careful path setup. Getting that ./chroma_db directory configured correctly was a small victory that felt huge at the time.

    What I Learned (The Hard Way)

    Building Faithly taught me some lessons that go beyond the technical details:

    Don’t Overengineer Early was probably the biggest one. Going straight to a VPS and manual configuration slowed me down significantly. Using managed services for the parts that weren’t core to my learning (like the blog) was a game-changer.

    Control the Controllables became my philosophy when dealing with the Bible’s massive scope. Nearly 800,000 words across 66 books meant I needed to be surgical about batching, error handling, and memory management. You can’t brute-force your way through datasets this large.

    Build in Resilience from day one. Crashes happen, APIs fail, servers reboot unexpectedly. Having a resume function didn’t just save hours of reprocessing time—it gave me the confidence to experiment knowing I could recover from failures.

    The Unexpected Wins

    Despite all the challenges, some things worked better than expected. The vector embeddings were surprisingly good at finding relevant verses, even for complex spiritual questions. The batching system, born out of necessity, actually made the whole process more stable and debuggable.

    Most importantly, I learned that building something real—even if it’s not perfect—teaches you more than any tutorial or course ever could.

    Coming Up Next

    In Part 3, I’ll dive into the theological minefield I walked into: how do you handle denominational differences when different Christian traditions interpret the same verses completely differently? Spoiler alert: it’s more complex than I thought.

    This is Part 2 of a 4-part series on building AI for spiritual conversations. What technical challenges have surprised you in your AI projects? Share your stories in the comments.

  • Building a Spiritual AI: What I Learned from My Epic Fail (Part 1 of 4)

    Building a Spiritual AI: What I Learned from My Epic Fail (Part 1 of 4)

    I’m not a developer. I can manage domains, install WordPress, and fumble my way through a database setup, but building an AI chatbot? That should have been a red flag. Instead, I thought: “I have Claude and ChatGPT – how hard could it be?”

    This is the story of how I tried to build an AI system for spiritual conversations, armed with nothing but basic technical skills, a lot of curiosity, and the naive belief that AI assistants could walk me through anything.

    The Perfect Learning Project (Or So I Thought)

    I wanted to build something to learn AI development, and biblical content seemed like the ideal starting point. The logic was simple:

    • Free, abundant source material: Thousands of years of text, all in the public domain
    • Well-structured content: Organized by books, chapters, and verses
    • Clear boundaries: A defined domain with established interpretive traditions
    • Meaningful purpose: Something that could actually help people

    What could be simpler than working with text that’s been around for millennia? I had visions of building something that could access multiple Bible translations, generate personalized devotionals, create study guides, and help with scripture interpretation. All powered by AI that would somehow “understand” the spiritual nuance.

    Looking back, I can see how that sounds a bit naive.

    My Technical Reality Check

    My actual technical background was pretty limited. I could:

    • Register domains and manage DNS settings
    • Install WordPress and mess around with themes
    • Set up basic databases (with a lot of Googling)
    • Copy and paste terminal commands when tutorials told me to

    That was about it. But I figured modern AI could bridge the gap. I’d seen people build impressive things with AI assistance, and I had both ChatGPT and Claude to help guide me through the technical pieces. I even wrote about setting up AI chatbots with DigitalOcean’s GenAI agents – which, looking back, should have been a clue that I was getting ahead of myself.

    My development process became a predictable cycle:

    1. Ask Claude or ChatGPT for step-by-step instructions
    2. Copy and paste the terminal commands they gave me
    3. Hit an error (this happened constantly)
    4. Copy and paste the error message back to the AI
    5. Try the suggested fix
    6. Repeat until something worked

    This actually got me surprisingly far. The AI assistants were patient with my questions, walked me through server setup, helped me understand APIs, and even explained basic programming concepts when I got stuck.

    The Moment Reality Hit

    But there’s a massive difference between “I got the code to run” and “I built something people can actually use.” That gap became painfully clear when I started calculating what this spiritual AI would actually cost to run.

    I was doing everything the hard way – spinning up VPS instances, uploading massive datasets, processing everything server-side. Meanwhile, ChatGPT had custom GPTs, OpenAI had APIs that could handle the heavy lifting, and there I was, burning through DigitalOcean credits like I was mining cryptocurrency.

    The math was brutal:

    • Storage costs for multiple Bible translations and commentaries
    • Processing power for real-time responses
    • Bandwidth for users actually using the thing
    • Backup and redundancy (because what if it crashes during someone’s spiritual crisis?)

    I was essentially rebuilding what already existed, but worse and more expensive. It was like deciding to build your own search engine instead of just using Google’s API.

    The Growing List of “Oh Wait” Moments

    As I dug deeper, the complexity kept multiplying:

    “Oh wait, different denominations interpret the same verses completely differently.”

    My simple Q&A bot would need to navigate centuries of theological debate. Catholic, Protestant, Orthodox, Baptist, Methodist – they don’t all agree, and for good reason.

    “Oh wait, people don’t just want facts – they want wisdom.”

    There’s a huge difference between “What does John 3:16 say?” and “What does John 3:16 mean for my life right now?” The first is a database query. The second requires understanding context, emotion, and spiritual discernment.

    “Oh wait, I’m not qualified to provide spiritual counsel.”

    What happens when someone asks the AI about depression, relationship problems, or losing their faith? Where’s the line between helpful information and pastoral care that should come from a real person?

    “Oh wait, getting the theology wrong could actually hurt people.”

    A bug in a weather app means someone brings an umbrella they don’t need. A bug in a spiritual AI could mislead someone about fundamental questions of faith, meaning, and morality.

    The AI Development Paradox

    Here’s what I learned about using AI to build AI: it’s incredibly powerful and fundamentally limited at the same time.

    Claude and ChatGPT were amazing at helping me with the technical implementation. They could generate code, debug errors, and explain concepts I’d never encountered. But they couldn’t solve the deeper problems:

    • How do you train an AI to be theologically accurate without being doctrinally rigid?
    • How do you handle the difference between information and wisdom?
    • How do you build something that’s helpful without overstepping into areas that require human pastoral care?
    • How do you serve people from different faith traditions without watering down the message?

    The AI assistants could help me build the technical infrastructure, but they couldn’t solve the fundamental challenge of creating artificial intelligence that could engage meaningfully with humanity’s deepest questions.

    What I Learned About Learning

    Building this spiritual AI taught me that some projects are deceptively simple on the surface but incredibly complex underneath. Biblical text might be freely available and well-organized, but the moment you try to help people engage with it meaningfully, you’re dealing with theology, psychology, pastoral care, and human nature.

    The technical challenges were solvable with enough persistence and AI assistance. The deeper challenges required wisdom I didn’t have and decisions I wasn’t qualified to make.

    But here’s the thing: I learned more from this “failed” project than I would have from building another generic chatbot. The complexity forced me to think deeply about what AI can and can’t do, what people actually need from technology, and where the boundaries should be.

    Coming Up in This Series

    Over the next several posts, I’ll walk you through the specific technical and practical challenges I encountered:

    • Part 2: The technical architecture decisions – RAG, fine-tuning, or something else entirely?
    • Part 3: Handling theological accuracy while respecting denominational differences
    • Part 4: What users actually asked (and what completely broke the system)
    • Part 5: The lessons learned and what I’d do differently next time

    If you’re thinking about building AI for complex, sensitive domains – whether spiritual, medical, legal, or educational – I hope this series helps you avoid some of the pitfalls I stumbled into.

    The good news? While I didn’t build the spiritual AI I originally envisioned, I learned enough to know what would actually be required to do it right. And that knowledge turned out to be far more valuable than the original project.

    This is Part 1 of a 4-part series on building AI for spiritual conversations. Have you tried building AI for complex domains? I’d love to hear about your experiences in the comments.

  • Turn ChatGPT Into Your Personal Game Master: The Fun World of AI-Powered Gaming

    Turn ChatGPT Into Your Personal Game Master: The Fun World of AI-Powered Gaming

    The Big Idea: You can transform ChatGPT into an amazing game master for RPGs, text adventures, and interactive stories using simple prompts. No coding required—just creativity and the right words.

    Remember those choose-your-own-adventure books from childhood? Artificial intelligence has brought them roaring back to life, and they’re better than ever. With the right prompts, you can turn ChatGPT or other AI chatbots into engaging game masters that create unique adventures every time you play.

    What Are AI Gaming Prompts?

    Think of prompts as instructions you give to AI to make it act like a specific character or run a particular type of game. Instead of getting generic responses, you get a tailored gaming experience that adapts to your choices.

    The AdmTal/chat-gpt-games collection on GitHub offers dozens of ready-to-use gaming prompts. Simply copy, paste, and start playing! From business simulations to mystery escape rooms, these prompts show just how creative AI gaming can get.

    Popular Game Types You Can Play Right Now

    Business Adventures: Pitch your startup idea on Shark Tank, negotiate deals at a pawn shop, or manage challenging retail customers. These games are surprisingly fun and teach real-world skills.

    Mystery & Escape Rooms: Get trapped in a basement and puzzle your way out, or solve crimes with limited clues. The AI’s ability to generate varied responses for the same prompt makes each playthrough unique and engaging.

    Historical Role-Play: Chat with Leonardo da Vinci about the Mona Lisa, or explore ancient civilizations. It’s like having a time machine powered by conversation.

    Fantasy Adventures: AI game masters can guide players through epic journeys filled with puzzles, choices, and dynamic challenges, tracking progress and adapting difficulty automatically.

    How to Get Started

    Getting into AI gaming is surprisingly simple:

    1. Pick a prompt from the chat-gpt-games repository that sounds interesting
    2. Copy and paste it into ChatGPT or your preferred AI chat
    3. Follow the AI’s lead and make choices as the story unfolds
    4. Experiment with different responses to see how the story changes

    Why AI Games Are So Engaging

    AI-driven text adventures are most interesting when the model generates multiple choices rather than letting you type anything. Part of the fun is discovering what creative options it’ll offer. This structure keeps stories coherent while giving you meaningful control over your adventure.

    Unlike traditional video games with predetermined storylines, AI games adapt in real-time. LLM-based generation adds an element of surprise and spontaneity, creating worlds that feel alive and unbound by static code. Your choices genuinely matter and lead to unique outcomes.

    Making Your Own Gaming Prompts

    Once you’ve tried existing games, creating your own is surprisingly rewarding. The key is being specific about:

    • The setting: Medieval tavern, space station, modern office
    • Your role: Detective, business owner, time traveler
    • The goal: Solve a mystery, make a deal, survive an adventure
    • The tone: Serious, comedic, mysterious

    The more context and detail you provide in your prompt, the more accurately the AI can create the experience you want.

    The Social Side of AI Gaming

    AI gaming isn’t just a solo activity. The r/ChatGPTGaming community is full of people sharing creative prompts, comparing adventures, and collaborating on new game ideas. It’s become a surprisingly social hobby where creativity and storytelling take center stage.

    What Makes This Different from Video Games?

    AI games offer something traditional video games can’t: infinite possibility within a framework. While video games have impressive graphics and complex mechanics, AI games excel at creative storytelling and adaptation. When you run games on natural language, it’s possible to invent new rules and approaches on the fly, making the experience feel genuinely dynamic.

    Plus, they work anywhere you can access a chatbot—your phone, computer, or tablet. No downloads, no installations, just pure interactive storytelling.

    The Future Looks Bright

    As AI gets better at understanding context and maintaining consistency, these games will only become more immersive. We’re already seeing prompts that remember your past choices and create ongoing storylines across multiple sessions.

    Whether you’re looking for entertainment, education, or just a creative outlet, AI-powered gaming offers an accessible way to experience interactive storytelling. The best part? The only limit is your imagination.

    Ready to start your adventure? Head over to https://github.com/AdmTal/chat-gpt-games, pick a prompt that catches your eye, and see where the story takes you. Your personal AI game master is waiting.

  • Why OpenAI’s API Studio is Your Next Favorite AI Toolkit

    Why OpenAI’s API Studio is Your Next Favorite AI Toolkit

    Ever wondered how to effortlessly integrate cutting-edge AI into your applications? OpenAI’s API Studio offers developers a powerful suite of tools designed to simplify AI integration, providing countless creative and practical options. Let’s explore why this platform might just become your go-to for AI-driven projects!

    A Versatile Playground for Developers

    OpenAI’s API Studio is ideal whether you’re building chatbots, analyzing images, automating tasks, or generating engaging content. Its intuitive design means anyone from beginners to seasoned developers can quickly get up to speed.

    Easy Text Generation

    Generate text effortlessly with support for popular programming languages like JavaScript, Python, or even command-line interfaces:

    • Quick Setup: Use official OpenAI SDKs (JavaScript, Python)
    • Rich Interactions: Easily craft dialogues, summaries, or stories in seconds.

    Example:

    from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-4.1",
    input="Write a catchy slogan for a coffee shop."
)

print(response.output_text)

    Advanced Computer Vision

    OpenAI’s API Studio extends beyond text, offering powerful computer vision capabilities. You can effortlessly analyze images for content, context, or even extract detailed information:

    import OpenAI from "openai";
const client = new OpenAI();

const response = await client.responses.create({
    model: "gpt-4.1",
    input: [
        { role: "user", content: "Identify objects in this image." },
        {
            role: "user",
            content: [{ type: "input_image", image_url: "image_url_here" }],
        },
    ],
});

console.log(response.output_text);

    Real-Time Information with Built-in Tools

    Stay informed and up-to-date effortlessly using integrated tools like web search:

    from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="gpt-4.1",
    tools=[{"type": "web_search_preview"}],
    input="What's trending in technology today?"
)

print(response.output_text)

    Speed and Performance at Its Best

    Deliver real-time AI experiences using streaming events and Realtime APIs. This allows your AI integrations to be fast, fluid, and responsive:

    const stream = await client.responses.create({
    model: "gpt-4.1",
    input: [{ role: "user", content: "Generate quick responses for chat." }],
    stream: true,
});

for await (const event of stream) {
    console.log(event);
}

    Intelligent Automation with AI Agents

    Leverage AI agents to automate complex workflows, manage tasks dynamically, and orchestrate multi-agent interactions seamlessly:

    from agents import Agent, Runner
import asyncio

sales_agent = Agent(
    name="Sales Agent",
    instructions="Handle sales inquiries.",
)

support_agent = Agent(
    name="Support Agent",
    instructions="Provide customer support.",
)

triage_agent = Agent(
    name="Triage Agent",
    instructions="Route tasks based on the user's query.",
    handoffs=[sales_agent, support_agent],
)

async def main():
    result = await Runner.run(triage_agent, input="I need help with my account.")
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

    Why Choose OpenAI’s API Studio?

    • User-Friendly: Designed with clarity, offering ease of use regardless of your expertise.
    • Versatility: From simple text queries to sophisticated automation, OpenAI’s API Studio has it all.
    • Speed and Efficiency: Minimize latency and deliver high-performance experiences.
    • Constant Innovation: Regular updates ensure you always have access to the latest AI advancements.

    Dive in today and transform your AI ambitions into reality with OpenAI’s versatile and powerful API Studio. Happy coding!

  • Building Your AI Chatbot Made Easy with DigitalOcean GenAI Agents

    Building Your AI Chatbot Made Easy with DigitalOcean GenAI Agents

    Ever wanted your own AI chatbot but dreaded the server management headache? DigitalOcean’s GenAI Platform takes away the hassle, letting you build a GPU-powered, smart chatbot in minutes—no Docker drama or server provisioning needed!

    Why Choose GenAI Agents?

    Fully Managed & Hassle-Free

    Forget scaling nightmares—click once, and DigitalOcean handles the GPUs, SSL, and scalability.

    Feature-Rich & Intuitive

    Leverage Retrieval-Augmented Generation (RAG), guardrails, and custom functions effortlessly. Create bots that understand context, stay safe, and respond accurately without juggling multiple services.

    Easy Embedding

    Copy-paste a ready-made JavaScript snippet into your WordPress or any webpage—no iframe tricks, just seamless integration.

    Let’s Build Your Bot—Step by Step

    Step 1: Set Up Your Project

    • Log into the GenAI Platform, click Projects → Create Project.
    • Name your chatbot (e.g., “JamesK-Chatbot”) and select your billing plan.

    Step 2: Create an Agent

    • Head to Agents → Create Agent.
    • Start from a useful template like “Customer Support” or “Business Analyst.”

    Step 3: Add Your Knowledge Base (RAG)

    • Under Knowledge Bases, connect your PDFs, Markdown files, or DigitalOcean Spaces.
    • Pick your embedding model (e.g., do-it-locally-002-vector).

    Step 4: Activate Guardrails

    • Enable guardrails like “Sensitive Data” and “Jailbreak Protection” to ensure conversations remain safe and focused.

    Step 5: Configure Functions & Routing

    • Use Functions & Routing to integrate helpful tools like weather checks or database queries.
    • Set rules to direct specific questions to the right agent, such as FAQs or orders.

    Step 6: Deploy with One Click

    • Hit Deploy, and DigitalOcean spins up your chatbot instantly, providing a secure endpoint and embed snippet.

    Embed Your Chatbot Anywhere

    Paste the provided JavaScript snippet into your WordPress theme (Custom HTML block or footer):

    <script src="https://genai.digitalocean.com/agent.js" data-agent-id="AGENT_ID_HERE"></script>

    Instantly enjoy a responsive, secure, dark-mode friendly chatbot on your site!

    Keep Your Chatbot Secure & Observed

    • Regularly rotate and restrict API keys in Settings → Access.
    • Use built-in monitoring dashboards to track chatbot performance and usage.

    Scaling and Advanced Features

    • Agents auto-scale—say goodbye to manual resizing.
    • Chain multiple agents for powerful workflows.
    • Bring your custom fine-tuned models or use DigitalOcean’s foundation models.

    Ready to Dive Deeper?

    Forget about infrastructure woes—go from zero to chatbot hero in under 15 minutes. Give GenAI Agents a whirl and tell me how your chatbot adventure goes!