Neural & Narrative

Tag: Claude

  • 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.