Upskilling Your Team in LLM Technologies

Transform Your Organization’s AI Capabilities Through Hands-On Learning

In an era where Large Language Models (LLMs) are reshaping how businesses operate, the gap between technological capability and practical implementation has never been wider. Your team knows AI matters. They’ve read the headlines, seen the demos, and understand the potential. But knowing about AI and knowing how to build with it are two entirely different challenges.

Far Horizons’ LLM Residency Program bridges that gap. This isn’t another webinar series or certification course. It’s an intensive, hands-on llm team training experience that embeds expert practitioners directly with your team to build real AI systems while systematically upskilling everyone involved.

The Challenge: Moving from AI Awareness to AI Capability

Most organizations face a common predicament. Leadership recognizes that LLM technologies represent a competitive advantage. Teams are eager to experiment. But the path from experimentation to production-ready systems is fraught with challenges:

Knowledge gaps: Understanding prompt engineering, retrieval-augmented generation (RAG), vector databases, and model selection requires specialized expertise most teams don’t yet possess.
Implementation uncertainty: How do you move from a ChatGPT proof-of-concept to a production system that reliably serves your customers?
Governance concerns: What are the security implications? How do you prevent data leakage? What about hallucinations and accuracy?
Resource constraints: Hiring AI specialists is expensive and time-consuming, and they’re in short supply.

The traditional consulting approach—flying in experts who deliver recommendations and leave—doesn’t solve the core problem. Your team still doesn’t know how to build, maintain, and evolve AI systems after the consultants depart.

AI upskilling must be embedded in the work itself.

What is the LLM Residency Program?

The LLM Residency Program is a 4-6 week embedded sprint where Far Horizons practitioners work side-by-side with your team to simultaneously deliver production-ready AI infrastructure and systematically upskill your entire organization.

Think of it as a combination of:

Technical implementation: Building real RAG pipelines, automation workflows, and AI-powered features your business actually needs
Knowledge transfer: Teaching your team prompt engineering, model behavior, architecture patterns, and best practices through doing, not just observing
Governance framework: Establishing security protocols, accuracy validation, and operational procedures that ensure your AI systems remain reliable and compliant

Since launching in 2023, the LLM Residency Program has upskilled over 5 teams across industries including automotive, real estate technology, healthcare, and professional services. Organizations consistently report 38% improvement in prompt success rates and dramatically accelerated AI adoption timelines.

Our Approach: No Guesswork, All Framework

Far Horizons operates on a simple philosophy: you don’t get to the moon by being a cowboy. Systematic innovation requires proven frameworks, measurable outcomes, and disciplined execution.

Every LLM residency follows a structured methodology:

Discovery Phase (Week 1): Assess your current AI maturity, identify high-value use cases, and establish success metrics
Foundation Build (Weeks 2-3): Implement core infrastructure—RAG pipelines, vector databases, API integrations, prompt templates
Team Enablement (Weeks 3-4): Hands-on workshops in prompt engineering, model evaluation, and system debugging
Production Deployment (Weeks 4-6): Launch working systems, establish monitoring, and transfer complete operational ownership to your team
Governance & Sustainability (Throughout): Document patterns, create runbooks, implement security protocols

This isn’t advice. It’s building systems together.

What Your Team Will Learn

For Technical Team Members

Prompt Engineering Mastery: Your developers will learn the art and science of crafting effective prompts—understanding few-shot learning, chain-of-thought reasoning, constraint specification, persona adoption, and iterative refinement. These aren’t theoretical concepts; they’re practical skills developed through building real features.

RAG Architecture & Implementation: Retrieval-Augmented Generation is the foundation of most enterprise LLM applications. Your engineers will gain hands-on experience with:

Vector database selection and optimization (Pinecone, Chroma, Weaviate)
Embedding strategies and semantic search
Chunking strategies for different content types
Context window management and relevance scoring
Hybrid search approaches combining semantic and keyword matching

Integration Patterns: Learn how to integrate LLMs into existing tech stacks using modern frameworks and APIs. Whether you’re working with Next.js, SvelteKit, Python, or other environments, you’ll understand the architecture patterns that make AI systems maintainable and scalable.

Model Selection & Evaluation: Not every problem requires the largest model. Your team will learn systematic approaches to model selection, cost optimization, latency management, and accuracy evaluation across providers (OpenAI, Anthropic, open-source alternatives).

System Debugging & Monitoring: When AI systems behave unexpectedly, how do you diagnose the issue? Your team will develop systematic debugging approaches, implement logging and monitoring, and establish feedback loops for continuous improvement.

For Non-Technical Team Members

Effective AI Communication: Product managers, designers, and business stakeholders will learn how to effectively communicate with AI systems—understanding what LLMs can and cannot do, how to frame problems for AI solutions, and how to evaluate AI-generated outputs.

Prompt Design Fundamentals: You don’t need to be a developer to craft effective prompts. Through structured exercises and real-world scenarios, non-technical team members learn to:

Structure clear, specific instructions
Provide relevant context efficiently
Iterate and refine based on outputs
Recognize and mitigate common failure modes

Use Case Identification: Which problems in your workflow are good candidates for LLM automation? Your team will develop intuition for identifying high-value AI applications, scoping realistic implementations, and avoiding common pitfalls.

Governance & Ethics: Understanding the limitations, risks, and ethical considerations of LLM technologies is crucial for everyone. Your entire team will learn to recognize hallucinations, understand data privacy implications, and implement responsible AI practices.

Hands-On, Practical Learning

The Far Horizons approach is built on a fundamental principle: you can’t learn to swim by reading about swimming.

Every concept introduced during the residency is immediately applied to real problems your organization faces. We don’t teach RAG pipelines through toy examples—we build the actual document search system your customer support team needs. We don’t lecture about prompt engineering—we optimize the prompts that generate your product descriptions.

Learning by Shipping

Week by week, your team builds tangible systems:

Week 1: A working prototype that demonstrates feasibility and builds confidence
Week 2: Core infrastructure that ingests your actual data and responds to real queries
Week 3: Production-ready features with proper error handling, logging, and user interfaces
Week 4: Deployed systems serving real users, with your team operating them independently

This approach creates what we call “learning by shipping”—the deepest understanding comes from taking complete ownership of working systems.

Bespoke Workshops & Masterclasses

Throughout the residency, Far Horizons delivers targeted workshops tailored to your team’s specific needs:

Prompt Engineering Masterclass: Advanced techniques for extracting maximum value from LLMs
RAG Deep Dive: Architecture patterns, optimization strategies, and troubleshooting
AI Governance Workshop: Security, compliance, accuracy validation, and risk mitigation
Model Behavior & Psychology: Understanding how different models “think” and respond

These aren’t generic presentations. Every workshop uses examples from your domain, addresses your specific challenges, and connects directly to the systems you’re building together.

Duration, Format, and Delivery

Standard Residency: 4-6 Weeks

The typical llm education services engagement runs 4-6 weeks, though duration adapts to your organization’s complexity and ambition:

4-week sprint: Focused implementation of a specific use case with core team upskilling
6-week comprehensive: Multiple use cases, broader team enablement, deeper governance implementation
Extended engagement: Some organizations opt for 8-10 week programs to cover multiple departments or complex integration requirements

Embedded, Collaborative Delivery

Far Horizons practitioners embed directly with your team—participating in standups, collaborating in your code repositories, pair programming with your engineers, and joining your planning sessions. We work in your tools, your codebase, your context.

This isn’t “consultants in a conference room.” It’s integrated collaboration.

Post-Geographic Flexibility

As a post-geographic company operating across 53 countries, Far Horizons brings world-class expertise regardless of your location. The residency model adapts to your preferred working style:

On-site intensive: Practitioners co-locate with your team for maximum immersion
Hybrid collaboration: Combination of on-site kickoffs and ongoing remote pairing
Fully distributed: Asynchronous-first approach optimized for global teams

The methodology remains consistent; the delivery format flexes to your needs.

Team Size & Composition

Residencies typically engage 4-10 team members directly, with knowledge radiating outward through documentation, demos, and cross-team presentations. The ideal composition includes:

2-4 engineers who will own the AI systems long-term
1-2 product/business stakeholders who define requirements
1-2 additional technical team members for broader knowledge distribution
Optional: designers, QA engineers, infrastructure specialists as relevant

Larger organizations sometimes run parallel residencies across multiple teams.

Designed for Everyone: Technical and Non-Technical Team Members

One of the most powerful aspects of the LLM Residency Program is its accessibility. While some AI training assumes advanced technical backgrounds, Far Horizons has developed approaches that work for diverse team compositions.

The Democratization of AI Capability

The reality is that effective AI implementation requires multiple perspectives:

Engineers need to understand architecture, integration, and optimization
Product managers need to identify use cases and scope implementations
Designers need to craft interfaces that surface AI capabilities effectively
Business stakeholders need to evaluate ROI and manage expectations
Compliance and legal need to understand governance implications

The residency creates a shared language and shared understanding across these roles. Technical and non-technical team members learn together, each gaining the knowledge relevant to their responsibilities.

Adaptive Learning Paths

Far Horizons tailors the learning journey to each participant:

Deep technical tracks for engineers building the systems
Applied prompt design for content creators and business users
Strategic AI literacy for leadership and decision-makers
Governance and compliance for legal and risk management teams

Everyone emerges with practical, applicable knowledge at the appropriate depth for their role.

LLM Adventure: Your Team’s First Step

Before or during the residency, team members often start with LLM Adventure—Far Horizons’ free, interactive game that teaches prompt engineering through a fantasy quest format.

Gamified Learning

Set in the mystical realm of Promptia, LLM Adventure guides players through 10 progressive levels that introduce core prompt engineering concepts:

The Basics: Understanding how LLMs interpret instructions
Structure & Clarity: Crafting well-formed prompts
Context & Examples: Providing relevant information efficiently
Iteration: Refining prompts based on outputs
Advanced Techniques: Few-shot learning, constraints, personas, chain-of-thought reasoning

Each level presents challenges that can only be solved by crafting effective prompts for an AI system. Players learn by doing, discovering techniques through experimentation in a safe, playful environment.

Accessible to Everyone

With no signup required and a completion time of approximately 30 minutes, LLM Adventure serves as an accessible introduction to AI for both technical and non-technical team members. Over 500 players have completed the adventure, many using it as a foundation before deeper technical training.

From Game to Production

The techniques learned in LLM Adventure directly translate to real-world applications. Players who complete the adventure arrive at the residency with shared vocabulary, basic intuition for model behavior, and confidence to experiment—accelerating the overall learning curve.

Organizations preparing for a residency often use LLM Adventure as a pre-engagement warm-up, ensuring every participant starts with baseline AI literacy.

Proven Outcomes: What Teams Achieve

Measurable Improvements

Organizations that complete the llm residency program consistently report significant capability gains:

38% improvement in prompt success rates: Teams craft more effective prompts faster, reducing iteration cycles and improving output quality
4-6 month acceleration in AI adoption timelines: What would have taken a year of experimentation happens in weeks
Production-ready systems: Every residency delivers working AI infrastructure, not just documentation
Sustained capability: Teams continue building and evolving AI systems independently after the residency concludes

Real-World Applications Delivered

Recent residencies have produced:

Intelligent document search for legal and compliance teams, enabling semantic search across thousands of contracts and regulations
Automated customer support systems that understand natural language queries and retrieve accurate responses from knowledge bases
Content generation pipelines for marketing teams, producing first drafts that humans refine
Data extraction automation that pulls structured information from unstructured documents
Conversational interfaces for internal tools, replacing complex UIs with natural language interactions

These aren’t prototypes or demos—they’re production systems serving real users and delivering business value.

Beyond Technical Skills

The transformation extends beyond code:

Organizational confidence: Leadership gains clarity on what AI can (and cannot) deliver, enabling better strategic decisions
Culture shift: Teams move from AI skepticism or hype to pragmatic, systematic implementation
Innovation capability: Organizations develop the muscle to identify, scope, and execute AI projects independently
Knowledge retention: Unlike traditional consulting, the capability stays with your team permanently

Why Far Horizons?

Field-Tested Expertise

Far Horizons founder Luke Chadwick has a 20-year track record of bringing emerging technologies to market—from co-founding REALABS at REA Group (pioneering VR/AR in Australian real estate) to building 360° capture platforms across Europe. The llm training methodology draws on decades of experience making new technologies practical and productive.

Post-Geographic Operations

Operating across 53 countries without a headquarters isn’t a lifestyle choice—it’s a competitive advantage. Far Horizons brings global perspective, cross-industry insights, and exposure to diverse AI implementations that enrich every residency.

Show, Don’t Tell Philosophy

The Far Horizons approach has always been “show, don’t tell.” Whether strapping colleagues to planks to demonstrate VR immersion or building RAG pipelines live in client codebases, the methodology centers on tangible demonstration and hands-on delivery.

This isn’t an agency that sends recommendations and leaves. This is practitioners who build alongside your team until you own the systems completely.

Innovation Engineered for Impact

Far Horizons operates on the principle that innovation without implementation is just theater. Every engagement focuses on measurable business outcomes, systematic approaches, and sustainable capability building.

You don’t get to the moon by being a cowboy.

Get Started: Transform Your Team’s AI Capability

The gap between AI potential and AI reality doesn’t close through reading or watching. It closes through systematic, hands-on implementation with expert guidance.

If your organization is ready to move from AI awareness to AI capability—if you want your team to build, maintain, and evolve production LLM systems with confidence—the LLM Residency Program offers a proven path forward.

Who Should Enroll

The residency is ideal for organizations that:

Recognize AI’s strategic importance but lack in-house LLM expertise
Have attempted AI projects that stalled due to knowledge gaps
Want to build sustainable AI capability rather than dependency on external consultants
Need production-ready systems and team upskilling simultaneously
Value systematic, disciplined approaches over experimental chaos

Next Steps

Explore LLM Adventure: Start with the free interactive game at farhorizons.io/adventure to experience Far Horizons’ teaching methodology firsthand.

Schedule a Consultation: Discuss your organization’s AI maturity, strategic objectives, and potential use cases. Far Horizons will assess whether the residency program aligns with your needs and propose a tailored engagement structure.

Plan Your Residency: Work with Far Horizons to scope the engagement—identifying key team members, prioritizing use cases, and scheduling the sprint that will transform your organization’s AI capabilities.

Contact Far Horizons

Ready to upskill your team in LLM technologies through hands-on, production-focused training?

Website: farhorizons.io Email: Contact via website inquiry form Location: Post-geographic (serving clients globally)

Frequently Asked Questions

How is this different from online AI courses or certifications?

Online courses teach concepts through isolated exercises. The LLM Residency teaches through building production systems your organization actually needs. Your team learns by doing real work, not completing tutorials.

Do we need existing AI expertise to benefit from the residency?

No. The program is designed to take teams from foundational understanding to production capability. Some technical background is helpful for engineers, but AI-specific expertise isn’t required.

What happens after the residency ends?

Your team owns the systems completely—code, infrastructure, documentation, and operational knowledge. Many organizations continue evolving their AI capabilities independently. Some engage Far Horizons for follow-up projects or ongoing advisory as new use cases emerge.

How do you ensure knowledge transfer, not just delivery?

Every implementation includes pair programming, workshop sessions, comprehensive documentation, and gradual responsibility transfer. By week 4-6, your team is operating the systems independently while Far Horizons observes and advises.

Can we focus on specific LLM applications relevant to our industry?

Absolutely. The residency is bespoke—every use case, example, and system built addresses your specific business needs and industry context.

What technology stack do you use?

Far Horizons works with modern stacks including Next.js, SvelteKit, Python, TypeScript, and GraphQL, integrating with LLM providers like OpenAI and Anthropic. The specific stack adapts to your existing infrastructure and preferences.

Is this suitable for regulated industries with strict compliance requirements?

Yes. Governance, security, and compliance are integrated throughout the residency. Far Horizons has experience with healthcare, financial services, and other regulated sectors where AI implementations require particular attention to data privacy and accuracy.

Transform your team’s AI capabilities. Build production systems. Establish sustainable practices. The LLM Residency Program delivers all three.