Future-Proof Data Models: Smart Migrations for AI Prompt Projects

[0:00]Mykola: Welcome back to the AI Prompt Stack podcast. If you’ve ever found yourself staring at a prompt workflow that just broke—again—because of a schema change, you’re in the right place. Today we’re diving deep into data modeling and migrations for AI prompt projects, and how to avoid those painful, time-consuming rewrites.

[1:10]Mykola: I’m your host, Alex Chen. With me is Jordan Patel, Lead AI Solutions Architect at PromptOps Consulting. Jordan, thanks for joining us.

[1:16]Jordan Patel: Thanks for having me, Alex. I’m excited. This is the kind of topic people only realize is critical after the pain hits.

[1:25]Mykola: Absolutely. We see so many teams racing ahead with new features, but the moment their data model can’t keep up, everything grinds to a halt. For listeners who aren’t deep in the weeds, can you describe what makes data modeling for AI prompt projects unique?

[2:10]Jordan Patel: Sure. In traditional apps, your data model is often pretty stable—users, products, orders, that sort of thing. But with AI prompt systems, your data isn’t just static fields; it’s often semi-structured, language-based, and changes as your prompts evolve. The relationships between prompts, inputs, outputs, and context can get messy fast.

[2:30]Mykola: So the schema isn’t just a table definition. It’s how prompts, versions, and context all fit together. And if you get it wrong up front?

[2:45]Jordan Patel: You’re in for a world of hurt. A small change—a new field in your prompt, a tweak to your context structure—can break downstream processes. Suddenly, last week’s data doesn’t fit, and your automation fails. I’ve seen teams forced into full rewrites just to get things running again.

[3:10]Mykola: Let’s pause and define a term: when we say ‘schema’ in this context, what exactly are we talking about?

[3:20]Jordan Patel: Great question. A schema here is the structure of your data—not just rows and columns, but how your prompts are organized, what context they accept, the format of outputs, and the metadata you store at each step. It’s the contract that every part of your pipeline relies on.

[4:00]Mykola: And when that contract changes, things break.

[4:05]Jordan Patel: Exactly. Especially if you haven’t planned for evolution. With classic CRUD apps, you can sometimes just add a new column and move on. With prompt-driven systems, even a subtle shift can mean downstream models, logging, or analytics stop working.

[4:15]Mykola: What are some ways you see prompt schemas get out of hand over time?

[4:35]Jordan Patel: One classic example is version sprawl. You start with one prompt format, then add a special case, then another. Suddenly, half your code is ‘if version equals X’ statements. Or, teams overload a single field to mean three different things, because changing the schema feels risky.

[5:10]Mykola: I’ve seen that—especially when deadlines are tight. Is there a particular story that illustrates the risks?

[5:30]Jordan Patel: Definitely. We worked with a team whose pipeline was built around a single prompt format. They needed to support a new use case, so they patched in a ‘mode’ field. But a few months later, they realized half their analytics queries were now ambiguous—was ‘mode’ a version, a type, or something else? When they tried to clean it up, it broke their model retraining scripts.

[6:10]Mykola: Ouch. So a quick fix today becomes a migration headache tomorrow.

[6:15]Jordan Patel: Exactly. And that’s common. The flexibility that makes prompt systems powerful also makes them fragile if you don’t design for change.

[6:20]Mykola: Let’s get practical. What are the most common triggers for migrations in prompt-driven systems?

[6:40]Jordan Patel: Usually it’s adding new context fields, changing the format of a prompt, or needing to support a new language or model version. Sometimes it’s regulatory—a new requirement to log extra metadata. All these force a change to your schema.

[7:00]Mykola: And if you don’t plan ahead, you’re stuck rewriting.

[7:10]Jordan Patel: Right. Or you build up layers of hacks and patches, and technical debt accumulates until nothing makes sense anymore.

[8:00]Mykola: Can you walk us through a real-world case where a schema migration broke a live pipeline?

[8:20]Jordan Patel: Sure. We had a client with a customer support prompt system. They added a new ‘urgency’ field, but forgot some older scripts were parsing prompt data by position, not by key. Overnight, thousands of responses were misclassified. It took days to track down, because the migration wasn’t coordinated or tested.

[9:10]Mykola: That’s a nightmare. What could they have done differently?

[9:25]Jordan Patel: A migration plan—mapping out which systems consume that data, testing with sample records, and rolling out the change in stages. And crucially, documenting the schema and its evolution, so everyone knows what to expect.

[9:50]Mykola: It sounds like the pain really comes when you underestimate the ripple effects.

[10:00]Jordan Patel: Absolutely. In prompt systems, downstream consumers might be scripts, dashboards, or even human reviewers. Any mismatch can break the chain.

[11:00]Mykola: Let’s talk about why migrations feel so much more painful in prompt projects compared to other apps.

[11:20]Jordan Patel: It comes down to unpredictability. Prompt structures aren’t as rigid as database schemas, but they’re still relied on by code and people. And because prompts change rapidly, migrations can happen before you’ve even finished cleaning up the last one.

[12:00]Mykola: So how do you balance the need to move fast with the risk of creating brittle systems?

[12:20]Jordan Patel: I’m a big believer in versioning—not just your code, but your prompt formats and data contracts. If you treat every schema change as a first-class event, you can plan for compatibility. That doesn’t mean never moving fast, but it means making change explicit.

[13:00]Mykola: Let’s dig into that. What does versioning look like in practice for prompts and data?

[13:20]Jordan Patel: You tag each prompt or schema with a version identifier. When you introduce a breaking change—say, changing a field name—you bump the version. Downstream systems can then check which version they’re dealing with and handle accordingly. This lets you run old and new formats side by side during a migration.

[13:50]Mykola: But isn’t there a cost to supporting multiple versions at once?

[14:05]Jordan Patel: For sure. There’s overhead in maintaining compatibility layers. But the alternative—breaking everything at once—is usually worse. If you architect for versioning, you can gradually migrate consumers, test each step, and avoid nasty surprises.

[14:30]Mykola: Do you ever recommend just patching instead of a proper migration?

[14:45]Jordan Patel: Sometimes! If it’s a truly isolated fix, a patch can be pragmatic. But every patch is a potential future migration in disguise. If you find yourself patching the same area twice, it’s probably time to step back and rethink.

[15:30]Mykola: I like that rule of thumb. Let’s talk through planning a migration. What should teams do before making the first change?

[15:50]Jordan Patel: First, map all consumers of your prompt data—scripts, dashboards, external APIs, even manual reviewers. Next, create a migration plan: what will change, how will data be transformed, and how will you test and roll back if needed. Communicate early and often with everyone affected.

[16:20]Mykola: Sounds like a lot of upfront work.

[16:30]Jordan Patel: It is, but it pays off. The teams that skip those steps end up spending triple the time fixing issues after the fact.

[17:10]Mykola: Can you share a mini case where a migration plan saved a team from a rewrite?

[17:30]Jordan Patel: Absolutely. We supported a fintech company that needed to add new risk assessment fields to their prompt logs. Instead of just tacking them on, they versioned the schema, built adapters for old and new formats, and set up shadow testing. When they flipped the switch, there were no surprises, and they avoided a major rewrite.

[18:00]Mykola: Shadow testing—so running both versions in parallel to catch issues?

[18:10]Jordan Patel: Exactly. It’s a safety net. You can compare outputs and ensure nothing critical breaks before fully migrating.

[19:00]Mykola: Let’s talk about testing and rollback. How much automation do you recommend?

[19:20]Jordan Patel: As much as you can afford! Automated tests for prompt formats, output validation, and migration scripts are invaluable. And always have a rollback plan—ideally, roll forward with a fix, but sometimes you need to revert fast.

[19:45]Mykola: What do you mean by 'roll forward with a fix'?

[20:00]Jordan Patel: If a migration goes wrong, instead of undoing everything, you apply a follow-up migration that fixes the new issues. It keeps your data moving forward, rather than risking corruption by reverting.

[20:20]Mykola: Are there situations where rolling back is the only option?

[20:30]Jordan Patel: If data is lost or fundamentally broken, yes. But with good tests and gradual rollouts, you can usually catch problems before they go live everywhere.

[21:30]Mykola: Let’s revisit the patching topic. Sometimes patching is necessary, but when does it cross into dangerous technical debt?

[21:50]Jordan Patel: If you’re patching around the same schema design over and over, that’s a red flag. Especially if no one can explain why a field exists, or you’re afraid to touch certain parts of the pipeline. That’s when it’s time for a proper migration, even if it’s painful.

[22:20]Mykola: Is there ever a case for living with technical debt in prompt systems?

[22:35]Jordan Patel: Sometimes, if the cost of migration outweighs the risk. But you should always document the debt, so future teams understand the trade-offs. Ignored debt becomes a ticking time bomb.

[23:30]Mykola: How do you recommend documenting evolving prompt data structures?

[23:50]Jordan Patel: Centralize your schema definitions, with clear version histories and migration notes. Use tools that let you generate docs from code or configs. And make sure every schema change is reviewed and logged—no silent updates.

[25:00]Mykola: Let’s talk about communication. How do you keep cross-functional teams aligned during migrations?

[25:20]Jordan Patel: Start early. Announce planned changes, share migration timelines, and create migration guides for users and developers. Have regular check-ins during the migration, and a clear channel for reporting issues. The more transparent you are, the smoother things go.

[25:50]Mykola: Do you ever see disagreements between engineering and, say, product or analytics teams about migration priorities?

[26:10]Jordan Patel: All the time. Product wants new features, analytics needs stable data, engineering wants to refactor. The key is to make migration risks and benefits clear to everyone. Sometimes you can compromise—stage changes, or provide temporary compatibility layers.

[26:50]Mykola: So it’s about surfacing trade-offs, not just technical arguments.

[27:05]Jordan Patel: Exactly. And sometimes you have to accept a little pain now to avoid a lot of pain later.

[27:20]Mykola: We’re just about halfway through. Jordan, can you recap the biggest lessons so far for listeners trying to avoid migration disasters in their AI prompt projects?

[27:30]Jordan Patel: Absolutely. Design for change from day one, document everything, plan and test migrations before you need them, and over-communicate with stakeholders. Most of all—don’t fear migrations. With the right approach, they’re a chance to make your system stronger, not just a source of pain.

[27:30]Mykola: Alright, so we've covered some foundational concepts and early lessons. I want to pivot a bit—let's dig into some real-world lessons. Can you share a case where a data model change in an AI prompt system led to unexpected complexity?

[27:47]Jordan Patel: Absolutely. There was a project where the team initially modeled prompts as just plain text, with a few metadata fields tacked on. It worked fine for prototypes, but as they scaled, they needed to support prompt templates, dynamic variables, and user-specific context. Suddenly, the simple model couldn't support the flexibility they needed. Retrofitting all that into production, especially for prompts already in use, was a nightmare.

[28:18]Mykola: What were some of the specific pain points in that retrofit?

[28:34]Jordan Patel: One big one: migrating existing prompt records safely. They had thousands of prompts live, and a lot of downstream dependencies—like analytics, prompt versioning, and user history. Updating the schema meant writing careful migration scripts and tons of regression tests, just to make sure nothing broke. And, honestly, some edge cases still slipped through.

[29:04]Mykola: That sounds intense. How did the team eventually resolve it?

[29:17]Jordan Patel: They moved to a more normalized model, separating prompt templates, variables, and user context into distinct tables or collections. They also started versioning prompts explicitly, so future changes could be handled with migrations and backward compatibility in mind. It was painful, but afterward, the system was way more resilient to change.

[29:44]Mykola: I like that. So, for listeners—don’t underestimate how tricky even small prompt changes can get. Let’s zoom out. What are some warning signs that your AI prompt data model might be too simplistic?

[30:01]Jordan Patel: A few big ones: If you’re duplicating logic everywhere to handle special cases, or if every new prompt type means refactoring your codebase, that’s a red flag. Also, if you find yourself storing JSON blobs or free-form text as a workaround, it’s probably time to rethink your model.

[30:24]Mykola: Great checklist. I want to bring in another story. Can you walk us through a time when a migration went well? Like, what did the team do differently?

[30:41]Jordan Patel: Sure! I worked with a team building a prompt management platform for customer support bots. Early on, they expected lots of prompt revisions, so they designed with migrations in mind. Every schema change was written as a migration script, versioned in code, and tested in a staging environment mirroring production data. When they needed to add new fields—like sentiment tags—they just added a migration, replayed it in staging, and only pushed to production after reviewing the results.

[31:13]Mykola: How did they handle old prompts that didn’t have those new fields?

[31:24]Jordan Patel: They used sensible defaults and a backward-compatible parser. So if an old prompt didn’t have a sentiment tag, the system treated it as 'neutral' until updated. No errors, no lost data.

[31:45]Mykola: That’s so important—backward compatibility isn’t just for APIs. It’s for your data, too. Let's dig into trade-offs. Is there ever a case for *not* versioning everything?

[32:02]Jordan Patel: Definitely. If you’re prototyping fast or building a throwaway experiment, heavy versioning can slow you down. But as soon as you have production users or integrations, versioning and migrations are worth the investment. It’s all about choosing the right level of rigor for your stage.

[32:22]Mykola: So it’s a spectrum. Don’t over-engineer too soon, but don’t ignore the long-term cost of rewrites. I want to pause for a rapid-fire segment—are you ready?

[32:36]Jordan Patel: Let’s do it!

[32:39]Mykola: Alright—favorite database for AI prompt projects?

[32:42]Jordan Patel: Document stores, like MongoDB—flexible for evolving schemas.

[32:46]Mykola: Biggest migration mistake you see?

[32:48]Jordan Patel: Skipping a dry run on production-like data.

[32:51]Mykola: How do you track prompt version history?

[32:54]Jordan Patel: Separate versioned collections or tables; never overwrite in place.

[32:57]Mykola: One thing to automate in every migration?

[32:59]Jordan Patel: Automated backups and rollback scripts.

[33:02]Mykola: Favorite testing approach for prompt migrations?

[33:05]Jordan Patel: Snapshot tests—catch subtle changes in prompt outputs.

[33:08]Mykola: What’s the most overlooked field in prompt data?

[33:11]Jordan Patel: User context—critical for personalization, but often skipped.

[33:14]Mykola: Last one: How often should you review your data model?

[33:17]Jordan Patel: Every major feature release or every few months—whichever comes first.

[33:23]Mykola: Love it. Let’s keep going! One thing I keep hearing from teams: 'We don’t have time to refactor.' What’s your advice when the team is in that crunch?

[33:39]Jordan Patel: I get it—time pressure is real. But my advice is to at least document known limitations. If you can’t fix the model now, flag the pain points and write up a migration plan for later. Technical debt is okay if it’s visible and managed.

[33:54]Mykola: So, don’t just sweep it under the rug. Make it visible for future devs.

[34:01]Jordan Patel: Exactly. And sometimes, just writing down the migration steps reveals shortcuts or partial fixes you can do even under time crunch.

[34:15]Mykola: Let’s shift to a mini case study. Can you tell us about a project where the team underestimated data migrations—and what happened?

[34:31]Jordan Patel: Yeah—there was an AI-powered content generator with a growing prompt library. They started with a flat file system—simple JSON files per prompt. No schema enforcement, just whatever fields the dev thought of at the time. When they tried to add prompt categories and localization, it was chaos. Updating thousands of files by hand wasn’t feasible, so they built ad-hoc scripts. But they missed edge cases—some prompts got corrupted, some lost their history, and rollback was a mess.

[35:05]Mykola: Ouch. How did they recover?

[35:15]Jordan Patel: Painfully. Eventually, they migrated all prompts into a database, wrote robust import and validation scripts, and enforced a common schema. They lost some data along the way, but it was a wake-up call.

[35:30]Mykola: So, if you’re listening and still on flat files, maybe now’s the time to invest in structure!

[35:35]Jordan Patel: Couldn’t agree more.

[35:39]Mykola: Let’s talk testing. What kinds of tests are most valuable when migrating prompt data?

[35:54]Jordan Patel: Regression tests for sure—make sure prompt outputs don’t change unexpectedly. Also, data integrity checks: every prompt should parse cleanly, and all required fields should be present. If your prompts power customer-facing tools, you want confidence nothing goes sideways.

[36:17]Mykola: Do you recommend manual spot checks, or is it all automated?

[36:25]Jordan Patel: A blend. Automation catches broad issues, but manual review can spot weird edge cases, like prompts with unusual formatting.

[36:35]Mykola: Let’s dig into backward compatibility. How do you keep old prompts working when the schema evolves?

[36:47]Jordan Patel: Design your loading code to tolerate missing or extra fields. If you add a field, default to a safe value. If you remove one, make sure code ignores it gracefully. And always keep a read-only backup of the previous version until you’re sure the migration worked.

[37:04]Mykola: That backup advice is golden. What about performance? Any gotchas when migrating lots of prompt data?

[37:17]Jordan Patel: Yeah—large migrations can spike database load or lock tables, which risks downtime. If possible, do batched migrations, run them during off-peak hours, and monitor system health throughout. And, if you can, rehearse with a snapshot of production data.

[37:37]Mykola: Let’s say you can’t afford downtime—what then?

[37:47]Jordan Patel: You might need a blue-green deployment: spin up the new model alongside the old, migrate traffic gradually, and keep both in sync until you’re confident. More work upfront, but safer for critical systems.

[38:03]Mykola: Interesting. I want to touch on team communication. How do you keep everyone aligned during a big data migration?

[38:16]Jordan Patel: Document the migration plan in detail, share it early, and hold a kickoff meeting. Assign clear owners for each migration step. And update stakeholders regularly—nobody likes surprises in production.

[38:33]Mykola: How do you handle downstream consumers—like analytics or reporting teams—when you update prompt schemas?

[38:46]Jordan Patel: Notify them in advance, provide sample data for testing, and if possible, maintain compatibility layers so their tools don’t break overnight. It’s a lot of coordination, but it pays off.

[39:02]Mykola: Let’s circle back to prompt versioning. Do you ever allow direct edits to live prompts, or is everything copy-on-write?

[39:13]Jordan Patel: For anything in production, we enforce copy-on-write. Every edit creates a new version, so you can always roll back or audit changes. For dev and staging, there’s more flexibility, but production needs that safety net.

[39:30]Mykola: Have you ever seen versioning go wrong?

[39:41]Jordan Patel: Yes! One team tried to version prompts by timestamp only, but didn’t link prompt uses to specific versions. Later, when prompts changed, they couldn’t trace which version had generated what output. Auditability was lost.

[39:59]Mykola: So, always link outputs to prompt versions for traceability.

[40:03]Jordan Patel: Exactly.

[40:07]Mykola: Alright, let’s do another case study. This one about internationalization. How do you model prompts that support multiple languages?

[40:22]Jordan Patel: Great question. One project I saw modeled each prompt as a parent entity, with child records for each language. That made it easy to add new translations and keep everything linked. But you have to be careful during migrations—if you add a new field, you need to ensure every language version gets it, or define sensible defaults.

[40:44]Mykola: Did you run into any surprises there?

[40:55]Jordan Patel: We realized that not all fields translated one-to-one. For example, some prompts had culture-specific examples. So, we ended up making 'localization' more than just translation—sometimes, it’s a whole new prompt structure per locale.

[41:13]Mykola: That’s a subtlety a lot of teams miss. I want to ask: How do you balance structure and flexibility in your prompt data model?

[41:24]Jordan Patel: Make core fields strict—like prompt text, variables, and language—but allow extension fields for team-specific needs. Use validation for the essentials, looser checks for add-ons. That way, you keep evolving without breaking core logic.

[41:41]Mykola: Are there any anti-patterns you see in AI prompt projects that listeners should watch for?

[41:52]Jordan Patel: Yes—storing everything as one big blob, or mixing logic and data. Another is not documenting prompt parameters—future devs get lost fast. And, of course, skipping backups before a migration.

[42:08]Mykola: How do you recommend documenting prompt schemas?

[42:19]Jordan Patel: A living schema spec, ideally co-located with the code. Use tools like OpenAPI or simple markdown docs, but make sure every field, type, and expected value is listed. And update it with every migration.

[42:37]Mykola: Alright, I want to do a quick thematic summary for listeners. Here’s what I’m hearing: model for change, version everything in production, automate your migrations, and always test with real data. Would you add anything?

[42:46]Jordan Patel: Yes—communicate early and often, and never underestimate the value of a good rollback plan.

[43:00]Mykola: Let’s wrap up with a practical segment. Can we walk through an implementation checklist for AI prompt data modeling and migrations?

[43:08]Jordan Patel: Definitely. Here’s what I recommend:

[43:13]Jordan Patel: First, define your prompt schema—core fields, variables, and metadata. Be explicit about what’s required.

[43:22]Jordan Patel: Second, decide on your storage model—document store or relational, depending on how complex your relationships are.

[43:30]Jordan Patel: Third, set up versioning—every change to a prompt or schema should be tracked and revertible.

[43:39]Jordan Patel: Fourth, write migration scripts for every schema change, and keep them in source control.

[43:48]Jordan Patel: Fifth, test migrations against a copy of production data before running them live.

[43:55]Jordan Patel: Sixth, automate backups and make sure you can restore quickly if needed.

[44:03]Jordan Patel: Seventh, communicate upcoming changes to the whole team and any consumers of your data.

[44:12]Jordan Patel: And finally—monitor for issues after migration, and keep a rollback plan handy until you’re confident.

[44:21]Mykola: That’s a solid checklist. I’d add: always review your model periodically, especially after major features or team changes.

[44:29]Jordan Patel: Absolutely. Prompt systems evolve fast—your model has to keep up.

[44:36]Mykola: I want to end with a final round of practical advice. If a team is about to embark on a big prompt migration, what’s your one must-do and one must-avoid?

[44:49]Jordan Patel: Must-do: dry run the entire migration on production-like data. Must-avoid: never skip the backup step, no matter how confident you are.

[45:02]Mykola: So true. Is there anything you wish more teams knew before they started building AI prompt systems?

[45:13]Jordan Patel: That prompt data is just as critical as code. Treat it with the same discipline—testing, versioning, code review. It’ll save so much pain later.

[45:27]Mykola: Before we close out, any recommended tools or resources for listeners wanting to level up their prompt data modeling?

[45:39]Jordan Patel: Look for schema migration libraries in your stack—many have great support now. Also, check out open-source prompt management tools for inspiration, and read up on database versioning best practices.

[45:55]Mykola: Great tips. We’re almost out of time, but I want to come back to the human side. Any advice for keeping teams motivated through a tough migration?

[46:07]Jordan Patel: Celebrate milestones, not just the final cutover. Break migrations into small, visible chunks so progress is clear. And remind everyone why the work matters—prompt quality directly impacts user experience.

[46:23]Mykola: That’s a great note to end on. Any final thoughts or words of encouragement for teams tackling this challenge?

[46:36]Jordan Patel: Even the best teams hit bumps with data modeling and migrations. Don’t be afraid to ask for help, learn from mistakes, and iterate. Every migration gets a little easier with experience.

[46:50]Mykola: Alright, let’s recap with a final checklist for listeners. Here’s what we covered today:

[46:56]Mykola: 1. Model your prompts for change and version them from day one.

[47:02]Mykola: 2. Write and test migration scripts before touching production.

[47:08]Mykola: 3. Communicate schema changes to all stakeholders.

[47:13]Mykola: 4. Always automate backups and have a rollback plan.

[47:18]Mykola: 5. Regularly review and evolve your data model.

[47:23]Mykola: Any final words to add?

[47:32]Jordan Patel: Just this: treat prompt data with respect. It’s at the heart of every AI system, and a little care up front prevents a lot of pain down the road.

[47:43]Mykola: Thank you so much for joining us and sharing your insights. Where can listeners find more of your work?

[47:53]Jordan Patel: I’m active on tech forums and occasionally write deep-dives on prompt engineering—search for my name and you’ll find me!

[48:00]Mykola: Fantastic. Thanks again for being here.

[48:07]Jordan Patel: Thanks for having me—it’s been a pleasure.

[48:13]Mykola: And for everyone listening, this has been another episode of Softaims. We hope you leave with practical steps to make your AI prompt projects more robust and maintainable.

[48:29]Mykola: If you enjoyed the show, please subscribe, share with your team, and leave us a review—it helps more people find us.

[48:39]Mykola: Until next time, keep building, keep learning, and don’t fear the migration. Goodbye!

[48:46]Jordan Patel: Goodbye everyone—happy prompting!

[48:54]Mykola: And we’re out. Thanks everyone for tuning in to Softaims. See you in the next episode.

[49:03]Mykola: Final reminder: check out the episode notes for links, resources, and the full implementation checklist. Take care!

[49:08]Jordan Patel: Take care!

[49:11]Mykola: [Outro music fades in]

[49:20]Mykola: This episode was produced by the Softaims podcast team. Special thanks to our guest, and to you for listening.

[49:29]Mykola: For more episodes, visit our website or follow us on your favorite platform.

[49:33]Mykola: [Outro music continues]

[49:36]Mykola: See you soon.

[49:40]Mykola: [Outro music fades out]

[55:00]Mykola: [End of episode]