# The $14M State Revolving Fund Application, Rebuilt in 6 Hours **By Dan Cumberland** · Published June 19, 2026 · Categories: AI Strategy > LLM application architecture is the system you build around a large language model— not the model itself. The model (Claude, GPT-4, whatever your team has... ## What Is LLM Application Architecture? LLM application architecture is the system you build around a large language model— not the model itself\. The model \(Claude, GPT\-4, whatever your team has been experimenting with\) is the engine\. The architecture is everything else: what information gets fed in, how it's retrieved, what the model is asked to do, and how the output is checked\. This distinction matters more than it sounds\. As Data Edge USA explains[3](/blog/blog-llm-application-architecture#ref-3), "Base LLM architecture defines how models learn\. Application architecture defines how organizations deploy and extend those models for production workflows\." You don't need to understand transformer internals to build something useful with a language model\. You do need to understand the system you're wrapping around it\. Most production LLM systems follow a layered pattern[4](/blog/blog-llm-application-architecture#ref-4): ```html-table
LayerWhat It DoesExample in AEC Context
User InterfaceHow people interact with the systemGrant writing tool, RFP assistant
OrchestrationSequences the stepsSends query → retrieves docs → calls model
Retrieval (RAG)Pulls relevant context from your document corpusFetches relevant SRF section templates
Language ModelGenerates the outputClaude, GPT-4, or similar
GuardrailsValidates output against rulesChecks for required compliance language
``` LLM application architecture encompasses all of these layers— the system built around the model, not the model itself\. Of these five layers, one matters more than most people expect\. And it's not the model\. ## The Four Components That Matter for Document\-Intensive Work For firms doing grant writing, proposal drafting, or compliance submissions, four architectural components determine whether an LLM application actually works: the retrieval layer, the orchestration layer, the context corpus, and the guardrails\. **1\. Retrieval\-Augmented Generation \(RAG\)** RAG is the dominant enterprise LLM application architecture pattern[4](/blog/blog-llm-application-architecture#ref-4)\. It pairs the model with a vector database— a system that stores your documents as searchable embeddings— and retrieves the most relevant ones when a query comes in\. As orq\.ai puts it[5](/blog/blog-llm-application-architecture#ref-5), "RAG doesn't assume the model has all the answers\. It goes and finds them\." The model doesn't need to memorize your SRF templates or compliance checklists\. It looks them up when it needs them\. **2\. The Orchestration Layer** The orchestration layer sequences the steps: retrieve documents, enrich with context, call the model, validate the output[4](/blog/blog-llm-application-architecture#ref-4)\. This is what turns a one\-shot chat prompt into a repeatable workflow— the difference between experimenting with AI and building something your team can run every time a proposal drops\. Think of it as the workflow manager sitting between your document library and the model\. For [AI agents and agentic systems](/blog/what-is-ai-agent), the orchestration layer controls what happens when the model needs to take multiple steps or query multiple sources— critical for complex compliance documents that draw from several regulatory frameworks simultaneously\. **3\. The Context Corpus** This is where most firms underinvest\. The context corpus is the document library the retrieval layer searches— your SRF templates, past applications, compliance checklists, engineering report formats\. Production LLM application complexity concentrates in the input enrichment and context layer[6](/blog/blog-llm-application-architecture#ref-6)\. A disorganized, incomplete, or outdated corpus produces unreliable output regardless of how sophisticated the model is\. We'll come back to this\. But corpus quality alone isn't the finish line\. **4\. Guardrails** Guardrails are validation layers that check output against rules— required compliance language, formatting requirements, red\-flag terms[7](/blog/blog-llm-application-architecture#ref-7)\. For AEC firms working on compliance\-sensitive documents, this is the layer that addresses the hallucination concern directly\. In an SRF context, a guardrail might flag a draft that's missing Davis\-Bacon certification language, or check that every referenced AIS\-compliant material is named explicitly\. Architecture mitigates hallucination; it doesn't eliminate it\. [AI governance and compliance](/blog/ai-governance-strategy) questions don't disappear because you've built a better system\. Human expert review of any AI\-generated compliance documentation remains non\-negotiable— but the guardrails layer means reviewers are catching missed requirements, not correcting fabricated ones\. But the most important architectural decision for most firms isn't the guardrails layer\. It's what comes before it\. ## The Insight That Changes How You Think About This The firms getting the most out of LLM application architecture are not the ones with the best prompts\. They're the ones with the best\-organized document libraries\. Fielding Jezreel has spent a decade writing federal grants\. He runs the Federal Grants Accelerator, a 12\-month learning community for grant professionals navigating complex compliance documentation— work that's structurally similar to SRF applications in its regulatory density and documentation burden\. When he started building AI tools into his practice, he expected the skill to be in crafting clever prompts\. What he discovered surprised him\. > "Prompting is so secondary\. Prompting looks cool, but really, you can be a bad prompter if your context is really, really good\." — Fielding Jezreel, Federal Grants Accelerator[8](/blog/blog-llm-application-architecture#ref-8) This isn't just a practitioner's gut feeling\. Production LLM architecture research confirms it: the complexity in enterprise LLM applications concentrates in the input enrichment and context layer[6](/blog/blog-llm-application-architecture#ref-6)— not in prompt sophistication\. The firms that win with this technology invest first in their document corpus: organized, current, retrieval\-ready archives of their best work\. The practical implication for AEC firms is direct\. Your biggest AI leverage point is probably your document library\. A decade of past SRF applications, compliance templates, engineering report formats— structured for retrieval, that's a genuine competitive edge\. [AI automation workflows](/blog/ai-automation-guide) don't start with tools\. They start with well\-organized information\. ## What This Looks Like for Firms Without a Development Team LLM application architecture exists on a spectrum\. At one end: a custom system built on Azure with a dedicated AI engineer\. At the other: a no\-code tool built on an afternoon\. Both use the same underlying patterns\. Pickaxe[9](/blog/blog-llm-application-architecture#ref-9) is a no\-code LLM application platform that implements RAG architecture visually— professionals without engineering backgrounds can build and deploy domain\-specific AI tools using knowledge bases they control\. Grant writers, proposal managers, and compliance specialists are using platforms like this for their first experiments— a simple grant narrative reviewer, a compliance checklist tool, a section boilerplate generator\. None of it requires a line of code\. The platform setup is genuinely fast\. The investment goes into building the document library it searches\. For AEC firms specifically, documented results exist at both ends of the spectrum\. According to the Messina Group[10](/blog/blog-llm-application-architecture#ref-10), an AEC services firm implemented a custom RAG application on Microsoft Azure, reducing proposal generation from "many hours to minutes\." Purpose\-built solutions are emerging too: according to Unanet[11](/blog/blog-llm-application-architecture#ref-11), their ProposalAI AEC product uses a custom model trained on DAWIA knowledge standards and FAR requirements, delivering proposal drafts 70% faster than traditional processes\. The right implementation tier isn't a budget decision— it's a compliance\-stakes decision\. The higher the regulatory consequences of an error, the more the architecture needs to support auditability and human expert review\. How do you choose? Complexity and compliance stakes are the deciding factors\. ```html-table
Starting PointPlatform TypeBest ForAEC Example
No codePickaxe, similarSimpler document tools, learning, low-stakesGrant narrative reviewer for internal use
Managed serviceUnanet ProposalAI, similarAEC-specific proposals with built-in complianceSF 330 federal proposals
Custom buildAzure/OpenAI + LangChainHigh-stakes, compliance-sensitive, full auditabilitySRF applications with NEPA + Davis-Bacon requirements
``` One honest note: the LLM tooling ecosystem is still maturing[6](/blog/blog-llm-application-architecture#ref-6), and [the hidden costs of AI projects](/blog/hidden-costs-ai-projects)— setup time, iteration, and validation— are real investments regardless of platform\. The firms that build these systems successfully front\-load that investment on the right thing— their document corpus— so the tool has good material to work with from day one\. ## The Human Layer LLM application architecture accelerates expert work\. It doesn't replace it\. The compliance knowledge, the regulatory relationships, the judgment calls that only come from doing this work for a decade— those stay human\. Fielding Jezreel said it plainly: > "It doesn't replace a grant writer\. I think the magic is when you've got someone with deep content expertise\. And you pair that with AI\. Neither one of those things, I think, are as strong alone\." — Fielding Jezreel, Federal Grants Accelerator[8](/blog/blog-llm-application-architecture#ref-8) That's the formula\. Domain Expertise \+ AI produces something neither creates alone\. A grant writer with LLM application architecture can serve more clients, bid more applications, and do better work\. A system without the grant writer's domain knowledge produces plausible\-sounding compliance language that might miss a state\-specific requirement buried on page 47\. Both parts are load\-bearing\. No matter the question, people are the answer\. The architecture just lets that expertise work at a different scale\. If mapping the right LLM application architecture to your specific document workflows feels like a full\-time job on its own— that's exactly the kind of problem an [AI implementation partner](/services/ai-implementation) can help you solve in a fraction of the time it would take to figure it out alone\. A good place to start regardless: audit your document corpus before you evaluate any tools\. That's where the leverage is\. ## FAQ ### What is LLM application architecture? LLM application architecture is the system built around a large language model— including the retrieval layer, orchestration, context corpus, and guardrails— as distinct from the internal architecture of the model itself[3](/blog/blog-llm-application-architecture#ref-3)\. The model \(Claude, GPT\-4, etc\.\) is the engine; the application architecture is everything that makes it useful for a specific workflow\. You don't need to understand how the model works to build production systems on top of it\. ### What is RAG in LLM applications? RAG stands for Retrieval\-Augmented Generation— the dominant enterprise LLM application architecture pattern[4](/blog/blog-llm-application-architecture#ref-4)\. It pairs a language model with a vector database that retrieves relevant context at inference time, so you get domain\-specific accuracy without retraining the model\. As orq\.ai describes it[5](/blog/blog-llm-application-architecture#ref-5), the system "doesn't assume the model has all the answers— it goes and finds them\." Your compliance documents and templates get retrieved when the model needs them, not memorized\. ### How does LLM application architecture address hallucination? The retrieval layer \(RAG\) grounds model responses in verified documents pulled from your corpus, reducing the probability of the model generating plausible\-but\-incorrect regulatory content[7](/blog/blog-llm-application-architecture#ref-7)\. The guardrails layer then validates outputs against rules, flagging required compliance language or prohibited terms[6](/blog/blog-llm-application-architecture#ref-6)\. Together, these layers reduce hallucination risk substantially\. They don't eliminate it\. Human expert review remains non\-negotiable for any AI\-generated compliance documentation\. ### Do you need developers to build LLM applications? Not for simpler implementations\. No\-code platforms like Pickaxe[9](/blog/blog-llm-application-architecture#ref-9) implement RAG architecture visually, and professionals without engineering backgrounds can build and deploy domain\-specific AI tools\. For complex, compliance\-sensitive applications— SRF grant packages, federal proposals requiring NEPA and Davis\-Bacon documentation— a production system on Azure with full auditability may be the right call[10](/blog/blog-llm-application-architecture#ref-10)\. The architectural patterns are the same at both ends of the spectrum; the implementation complexity varies\. ### How is LLM application architecture used in AEC firms? AEC firms are using RAG\-based LLM applications to automate proposal and grant application drafting\. The Messina Group documented an AEC firm reducing proposal generation from hours to minutes using a custom RAG application on Microsoft Azure[10](/blog/blog-llm-application-architecture#ref-10)\. Purpose\-built solutions like Unanet ProposalAI are trained on AEC\-specific compliance requirements and report 70% faster proposal delivery[11](/blog/blog-llm-application-architecture#ref-11)\. The use cases concentrate in document\-intensive workflows: SF 330 proposals, technical narratives, compliance checklists\. ## References 1. U\.S\. Environmental Protection Agency, "Clean Water State Revolving Fund \(CWSRF\)" \(2025\) — [https://www\.epa\.gov/cwsrf](https://www.epa.gov/cwsrf) 2. U\.S\. Environmental Protection Agency, "Clean Water State Revolving Fund \(CWSRF\) Implementation" \(2025\) — [https://www\.epa\.gov/cwsrf/clean\-water\-state\-revolving\-fund\-cwsrf\-implementation](https://www.epa.gov/cwsrf/clean-water-state-revolving-fund-cwsrf-implementation) 3. Data Edge USA, "What Is LLM Architecture? A Guide for Modern AI Systems" \(2025\) — [https://www\.dataedgeusa\.com/what\-is\-llm\-architecture/](https://www.dataedgeusa.com/what-is-llm-architecture/) 4. DZone, "Enterprise LLM Architecture Patterns: RAG to Agentic Systems" \(2025\) — [https://dzone\.com/articles/llm\-architecture\-patterns\-rag\-to\-agentic](https://dzone.com/articles/llm-architecture-patterns-rag-to-agentic) 5. orq\.ai, "RAG Architecture Explained: A Comprehensive Guide" \(2026\) — [https://orq\.ai/blog/rag\-architecture](https://orq.ai/blog/rag-architecture) 6. ZenML, "Building Production\-Grade LLM Applications: An Architectural Guide" \(2025\) — [https://www\.zenml\.io/llmops\-database/building\-production\-grade\-llm\-applications\-an\-architectural\-guide](https://www.zenml.io/llmops-database/building-production-grade-llm-applications-an-architectural-guide) 7. Zekeriya Besiroglu / Medium, "Architecture Patterns for LLM Systems" \(2025\) — [https://medium\.com/@zekeriyabesiroglu/architecture\-patterns\-for\-llm\-systems\-83322b1dd537](https://medium.com/@zekeriyabesiroglu/architecture-patterns-for-llm-systems-83322b1dd537) 8. Fielding Jezreel, Federal Grants Accelerator, proprietary interview \(September 2025\) — Internal: ClientData/ProprietaryContent/SourceMaterials/fielding\_jezreel/interview\_transcript\.md 9. Pickaxe, "Build, Share, and Manage AI Apps" \(2025\) — [https://home\.pickaxeproject\.com/old\-home\-2](https://home.pickaxeproject.com/old-home-2) 10. Messina Group, "AEC Services Firm Revolutionizes their Proposal Process with Custom Generative AI \(RAG\) Application" \(2025\) — [https://messinagroupinc\.com/client\-success\-stories/data\-analytics\-ai/aec\-services\-firm\-revolutionizes\-their\-proposal\-process\-with\-custom\-generative\-ai\-rag\-application/](https://messinagroupinc.com/client-success-stories/data-analytics-ai/aec-services-firm-revolutionizes-their-proposal-process-with-custom-generative-ai-rag-application/) 11. Unanet, "ProposalAI AEC" \(2025\) — [https://unanet\.com/proposal\-ai\-aec](https://unanet.com/proposal-ai-aec) --- Source: https://dancumberlandlabs.com/blog/llm-application-architecture/