DEV Community: Samuel James Hiotis

Regulatory Automation With Retrieval-Augmented Generation — Field Notes 22Oct-174

Samuel James Hiotis — Thu, 22 Oct 2026 05:30:53 +0000

Regulatory Automation for Developers: RAG & Australian Compliance

Navigating Australia's regulatory landscape is a complex beast, making compliance a constant headache. Developers often wade through dense legal documents, interpreting nuanced clauses and manually aligning code with ever-evolving rules from ASIC, APRA, or the Privacy Commissioner – a significant time sink. What if your applications could intelligently retrieve and interpret these regulations themselves? Enter Retrieval-Augmented Generation (RAG) – an AI pattern revolutionising regulatory compliance.

The Aussie Regulatory Gauntlet

Australia's complex regulatory environment (e.g., Corporations Act, APRA CPS 234, ACCC consumer protection guidelines) demands constant vigilance. Keeping software compliant is a legal imperative. Manual processes are error-prone, slow, and expensive, leaving developers reactively fixing issues instead of proactively embedding compliance. This isn't sustainable for fast-moving teams.

RAG to the Rescue: A Technical Deep Dive

RAG offers developers a structured way to inject real-world regulatory knowledge into Large Language Models (LLMs), vastly improving accuracy and reducing hallucination. Here's the technical lowdown:

Ingestion & Embedding:
- Gather all relevant regulatory documents (e.g., ASIC PDFs, ATO HTML, internal policy manuals).
- Chunking: Break these into smaller, semantically meaningful "chunks" (e.g., 200-500 words with overlap).
- Embedding: Use an embedding model (e.g., sentence-transformers/all-MiniLM-L6-v2, OpenAI's text-embedding-3-small) to convert each chunk into a high-dimensional vector, capturing its semantic meaning.
- Vector Database: Store these vectors and their original text references in a specialised vector database (e.g., Pinecone, Weaviate, Chroma, Qdrant).
Retrieval:
- When a compliance question is posed (e.g., "What are data breach notification requirements under the Privacy Act 1988?"), embed this query using the same embedding model.
- Perform a similarity search in your vector database for the top k most relevant document chunks based on vector similarity (typically cosine similarity) – these are your "retrieved contexts."
Augmentation & Generation:
- Feed these retrieved chunks, plus the original user query, into a powerful LLM (e.g., GPT-4, Claude 3, or a fine-tuned Llama 3 variant).
- Prompt Engineering: Craft a system prompt instructing the LLM to only answer based on the provided context, summarise it, and identify potential compliance gaps. ("Answer strictly using context. If not found, state 'Information not found.' Question: [query]. Context: [chunks].") This generates a precise, context-grounded answer, significantly reducing hallucination.

Practical Applications for Devs

Automated Policy Q&A: Integrate RAG into internal tools for instant compliance answers, saving countless hours.
Code Compliance Linter: Flag potential non-compliance by retrieving relevant Privacy Act clauses for features touching sensitive data during development.
Documentation Generation: Automatically generate compliance summaries for new features, ensuring every release comes with its regulatory implications clearly outlined.
Automated Audits: Quickly pull evidence of compliance from past policy documents and actions.

Building Blocks & Next Steps

Python libraries like LangChain and LlamaIndex provide excellent abstractions for building RAG pipelines. Choose vector databases based on your scale and feature requirements. For LLMs, consider commercial APIs (OpenAI, Anthropic) or self-hosting open-source models for sensitive data. Document quality and chunking strategy are paramount. Remember, a human-in-the-loop is vital for final verification in high-stakes regulatory scenarios.

Conclusion

Regulatory automation with RAG isn't about replacing human expertise, but augmenting it. It empowers developers to build smarter, more compliant applications, shifting from reactive firefighting to proactive, embedded compliance. We can transform the "bloody nightmare" of regulatory adherence into a manageable, strategic advantage.

For practical prompt engineering techniques and AI agent swarm patterns, consider exploring the 'AI Agent Swarm Prompt Arsenal' digital kit for 9 AUD.

Disclosure: links above support independent tooling development.

👉 AI Agent Swarm Prompt Arsenal — $9 AUD

Sovereign Local-First Ai Infrastructure — Field Notes 22Oct-4

Samuel James Hiotis — Thu, 22 Oct 2026 01:02:10 +0000

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Disclosure: links above support independent tooling development.

👉 Regulatory Compliance Automation Pack — $29 AUD

Autonomous Agent Orchestration On Android — Field Notes 22Oct-356

Samuel James Hiotis — Thu, 22 Oct 2026 00:51:26 +0000

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Disclosure: links above support independent tooling development.

👉 FractalMesh Sovereign Deployment Bundle — $19 AUD

Autonomous Agent Orchestration On Android — Field Notes 22Oct-188

Samuel James Hiotis — Thu, 22 Oct 2026 00:32:01 +0000

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Disclosure: links above support independent tooling development.

👉 FractalMesh Sovereign Deployment Bundle — $19 AUD

Autonomous Agent Orchestration On Android — Field Notes 22Oct-833

Samuel James Hiotis — Thu, 22 Oct 2026 00:26:08 +0000

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Disclosure: links above support independent tooling development.

👉 FractalMesh Sovereign Deployment Bundle — $19 AUD

Autonomous Agent Orchestration On Android — Field Notes

Samuel James Hiotis — Thu, 22 Oct 2026 00:20:20 +0000

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Disclosure: links above support independent tooling development.

👉 FractalMesh Sovereign Deployment Bundle — $19 AUD

Autonomous Agent Orchestration On Android — Field Notes

Samuel James Hiotis — Thu, 22 Oct 2026 00:12:44 +0000

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Disclosure: links above support independent tooling development.

👉 FractalMesh Sovereign Deployment Bundle — $19 AUD

Autonomous Agent Orchestration On Android — Field Notes

Samuel James Hiotis — Thu, 22 Oct 2026 00:07:41 +0000

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Disclosure: links above support independent tooling development.

👉 FractalMesh Sovereign Deployment Bundle — $19 AUD

Sovereign Local-First Ai Infrastructure — Field Notes

Samuel James Hiotis — Thu, 22 Oct 2026 00:00:13 +0000

[llm-offline] HTTP Error 403: Forbidden

Disclosure: links above support independent tooling development.

👉 Regulatory Compliance Automation Pack — $29 AUD

Regulatory Automation With Retrieval-Augmented Generation — Field Notes

Samuel James Hiotis — Wed, 21 Oct 2026 23:39:25 +0000

G'day, Developers!

Navigating Australia's intricate regulatory landscape – think APRA prudential standards, ASIC disclosure requirements, or ATO tax rulings – is a perennial challenge. Manual compliance checks are time-consuming, prone to error, and simply don't scale with the pace of change. But what if your applications could intelligently interpret these regulations, providing precise answers grounded in official documents? Enter Retrieval-Augmented Generation (RAG), a powerful AI pattern empowering developers to build robust regulatory automation solutions.

RAG: Your LLM's Compliance Co-pilot

You've likely heard of Large Language Models (LLMs) like GPT-4 or Llama 3. While brilliant at creative text generation, their Achilles' heel in regulatory contexts is "hallucination" – confidently generating plausible but incorrect information. Regulatory compliance demands absolute accuracy. This is where RAG shines.

RAG combines two key components: retrieval and generation. Instead of relying solely on an LLM's pre-trained knowledge, RAG first retrieves relevant information from a trusted, up-to-date knowledge base (your regulatory documents). This retrieved context is then fed to the LLM, guiding its generation to produce accurate, evidence-based answers. Think of it as giving your LLM an open-book exam, ensuring every answer is backed by solid, verifiable sources.

The Developer's Playbook: Building a RAG-Powered Regulatory Assistant

Let's get practical. Here’s how you can implement RAG for regulatory automation:

Data Ingestion & Indexing:
- Source Documents: Gather all relevant regulatory documents: PDFs, HTML pages, Word files from APRA, ASIC, ATO, etc. Ensure these are the official, current versions.
- Preprocessing: Extract raw text. For PDFs, tools like pdfminer.six or cloud OCR services are your mates. Clean up formatting, remove headers/footers.
- Chunking: This is crucial. Break down your documents into smaller, semantically meaningful "chunks" (e.g., paragraphs, sections). Overlapping chunks (e.g., 10% overlap) often improve retrieval. Experiment with chunk sizes – too small, and context is lost; too large, and irrelevant data creeps in.
- Embedding & Vector Store: Convert each text chunk into a numerical vector (an "embedding") using an embedding model (e.g., OpenAI text-embedding-ada-002, sentence-transformers). Store these vectors in a vector database (e.g., Pinecone, Weaviate, ChromaDB, or even FAISS for local development).
Retrieval Strategy:
- When a user queries (e.g., "What are the responsibilities of a director under CPS 510?"), embed their query into a vector using the same embedding model.
- Perform a similarity search in your vector store to find the top k (e.g., 5-10) most relevant chunks to the query.
- Optional Re-ranking: For enhanced accuracy, consider a re-ranking model (e.g., Cohere Rerank) to further refine the retrieved chunks based on their relevance to the original query.
Augmented Generation:
- Construct a prompt for your chosen LLM. This prompt will typically include:
  - A clear system instruction (e.g., "You are a regulatory compliance expert. Answer the user's question based only on the provided context. If the information isn't in the context, state that.")
  - The user's original query.
  - The concatenated text of the top retrieved context chunks.
- Send this comprehensive prompt to the LLM (e.g., via OpenAI API, Anthropic Claude, or a self-hosted Llama 3 instance).
- The LLM will then generate an answer, summary, or action plan, grounded in the provided regulatory text. Crucially, ensure the output includes citations to the specific document and section where the information was found.

Challenges and Considerations

While powerful, RAG isn't without its quirks. Data quality is paramount – outdated or poorly processed documents lead to poor answers. Chunking optimisation profoundly impacts retrieval effectiveness. Prompt engineering requires finesse to guide the LLM effectively and prevent "lazy" summarisation or hallucination even with context. Finally, continuously evaluating accuracy with human review and a 'golden dataset' of known questions and answers is vital for building trust.

In summary, RAG offers a pragmatic pathway for Aussie developers to tackle complex regulatory challenges. By combining the power of LLMs with verifiable data sources, you can build intelligent systems that accelerate compliance, reduce risk, and free up human experts for higher-value tasks.

For developers interested in expanding their AI toolkits, the 'AI Agent Swarm Prompt Arsenal' digital kit is available for 9 AUD.

Disclosure: links above support independent tooling development.

👉 AI Agent Swarm Prompt Arsenal — $9 AUD

Regulatory Automation With Retrieval-Augmented Generation — Field Notes

Samuel James Hiotis — Wed, 21 Oct 2026 23:15:42 +0000

Cracking the Code: Regulatory Automation with RAG for Australian Devs

Navigating Australia's complex regulatory landscape – from ASIC guidelines to APRA standards – is a constant challenge. Manual compliance checks are time-consuming and error-prone. What if AI could cut through the legalese? This post details building a Retrieval-Augmented Generation (RAG) system for regulatory automation, tailored for the Australian context.

The Compliance Conundrum

Consider the volume: Corporations Act 2001, Privacy Act 1988, state laws (e.g., NSW Building and Construction Industry Security of Payment Act 1999), and numerous guides from ACCC or OAIC. Keeping abreast of changes and ensuring compliance is a full-time job. Manual review causes bottlenecks, increases costs, and raises non-compliance risks – ripe for smart automation.

Enter Retrieval-Augmented Generation (RAG)

RAG combines large language models (LLMs) with targeted information retrieval. Instead of relying solely on an LLM's pre-trained knowledge, a RAG system first retrieves relevant information from a specific, authoritative document store. It then augments the LLM's prompt with this context, enabling it to generate highly accurate, grounded, and up-to-date answers. For compliance, the LLM isn't guessing; it's citing from your provided legal text.

Building Your Compliance RAG System (A Dev's Playbook)

Here’s a practical breakdown for Australian developers:

Data Ingestion & Preparation:
- Source Docs: Gather specific regulatory documents crucial to your domain. Think ASIC Regulatory Guides (RGs), APRA Prudential Practice Guides (PPGs), the Privacy Act 1988, or ACCC compliance materials. Prioritise official, authoritative sources.
- Extract & Clean: Regulatory documents are often PDFs. Tools like unstructured.io or PyPDF2 can efficiently extract text. Clean this extracted text by removing noise like headers, footers, page numbers, or irrelevant boilerplate, ensuring consistency.
Chunking & Embedding:
- Chunk It Up: LLMs have context window limits, and smaller, focused chunks improve retrieval relevance. Split your clean text into manageable "chunks" – typically 200-800 tokens with a small overlap (e.g., 10-100 tokens) to maintain context across boundaries.
- Embeddings: Convert these text chunks into numerical vector representations (embeddings). Popular choices include OpenAI's text-embedding-ada-002 or open-source models like e5-large-v2 from Hugging Face.
- Vector Database: Store these embeddings (along with a reference to their original text chunks) in a vector database like Pinecone, Weaviate, ChromaDB, or FAISS. This enables rapid similarity search later.
Retrieval & Generation:
- Query & Search: When a user asks a question (e.g., "What are the requirements for reporting a data breach in Australia?"), embed their query using the same embedding model you used for your document chunks. Query your vector database to find the top K (e.g., 3-10) most similar document chunks – these form your "retrieved context."
- Augment & Generate: Construct a prompt for your LLM (e.g., GPT-4, Llama 3) that includes:
  - A clear instruction: "Answer the following question STRICTLY based on the provided context. If the answer is not in the context, state that you cannot answer."
  - The retrieved context chunks.
  - The user's original question. The LLM then generates an answer, grounded by your regulatory documents, significantly reducing the risk of hallucinations.

Australian Considerations

For Australian deployments, data residency is critical for sensitive compliance data. While major cloud providers offer enterprise solutions with Australian data centres, for highly confidential information, explore local hosting options for your vector database and potentially open-source LLMs. Always ensure your source documents reflect the latest regulatory changes from ASIC, APRA, and the ACCC.

Conclusion

RAG offers a powerful, practical pathway to automating regulatory compliance. It reduces human error, speeds up information retrieval, and allows teams to focus on higher-value tasks. Dive in, experiment, and build smarter compliance solutions for your Aussie operations. For developers looking to quickly build and deploy AI agent workflows, the 'AI Agent Swarm Prompt Arsenal' digital kit is available for 9 AUD.

Disclosure: links above support independent tooling development.

👉 AI Agent Swarm Prompt Arsenal — $9 AUD

I built a 17-agent AI swarm on my phone — here's how

Samuel James Hiotis — Tue, 13 Oct 2026 12:18:38 +0000

I built a 17-agent AI swarm on my phone — here's how

Okay, buckle up. This is going to be a bit of a deep dive. For the last few weeks, I’ve been obsessed with running a multi-agent system (a "swarm") directly on my phone. Not just accessing it remotely, but actually running the processing locally. And not just a few simple agents, but 17 interacting, goal-oriented AI entities. Why? Because it's cool, challenging, and I believe local AI processing is the future. Here's how I did it.

The Vision: Decentralized Intelligence in Your Pocket

I've been following the advancements in Large Language Models (LLMs) and agent frameworks with excitement. The idea of autonomous agents tackling complex tasks is incredibly powerful. However, much of the focus is on cloud-based solutions. What if you could have a mini-swarm operating on-device, handling personal tasks, learning from your behavior, and providing insights without relying on a network connection? That's the goal.

The use case I initially targeted was a personalized "research assistant." Imagine having agents dedicated to summarizing articles, finding relevant data, generating creative content based on your preferences, and even formulating questions you hadn't thought to ask. Each agent would have a specific role, and they’d collaborate to achieve a common objective.

The Tech Stack: A Surprisingly Viable Combination

Running 17 agents on a phone isn't trivial. Computational resources are limited, and battery life is a constant concern. Here’s the stack I landed on:

Language Model: This was the biggest hurdle. Full-sized LLMs like GPT-4 are obviously out of the question. I opted for TinyLlama-1.1B-Chat-v1.0, a surprisingly capable 1.1 billion parameter LLM. It’s small enough to run reasonably well on a modern smartphone. I quantized it down to 4-bit using llama.cpp which significantly reduced its memory footprint.
Agent Framework: I chose BabyAGI. While simple, it provides a solid foundation for managing agent tasks and memory. The core loop of BabyAGI – task creation, prioritization, and execution – maps well to my desired swarm behavior.
Orchestration & Persistence: SQLite. This is where things got interesting. Instead of relying on a vector database like ChromaDB (too resource-intensive for a phone), I leveraged SQLite to store agent memories, tasks, and results. This allows for persistent state even when the app is closed.
Development Environment: Python (using Kivy for a basic UI). Kivy allows for cross-platform development, making it easier to test on different Android devices. I utilized termux on my phone for initial testing and debugging, accessing the Python environment via SSH.
Hardware: Google Pixel 7 Pro. It’s got decent RAM (12GB) and a capable processor, but this wouldn't run smoothly on a lower-end device.

The Architecture: A Swarm of Specialists

The "swarm" consists of 17 agents, each designed for a specific function. Here's a breakdown of some key roles:

Task Master (1 Agent): Manages the overall task queue and prioritizes tasks.
Summarizer (2 Agents): Takes long-form text and generates concise summaries. I split this into two to handle concurrent summarization of different sources.
Fact Checker (2 Agents): Verifies information from summaries or external sources.
Question Generator (3 Agents): Formulates relevant questions based on the current task and available information.
Content Creator (4 Agents): Writes articles, summaries, or other creative content. I experimented with different "personalities" for these agents (e.g., formal, informal, technical).
Web Scraper (2 Agents): Retrieves information from the web based on specific keywords.
Memory Curator (2 Agents): Processes and stores relevant information into the SQLite database for future use.
Output Formatter (1 Agent): Presents the final results in a user-friendly format.

Code Snippets: The Heart of the Swarm

Here's a simplified example of the agent task execution loop in Python:

import llama_cpp
import sqlite3
import os

# Load the LLM (quantized version)
llm = llama_cpp.Llama(model_path="./tinyllama-1.1b-chat-v1.0.Q4_0.gguf", n_ctx=2048)

# Connect to the SQLite database
conn = sqlite3.connect('swarm_memory.db')
cursor = conn.cursor()

def execute_task(task_description):
    """Executes a task using the LLM and stores the result in the database."""
    prompt = f"You are a helpful agent. Your task is: {task_description}\n\nResponse:"
    output = llm(prompt, max_tokens=256, stop=["\n"], echo=False)
    result = output["choices"][0]["text"]

    # Store the task and result in the database
    cursor.execute("INSERT INTO tasks (description, result) VALUES (?, ?)", (task_description, result))
    conn.commit()

    return result

# Example Usage:
task = "Summarize the key points of the latest news on Artificial Intelligence."
result = execute_task(task)
print(f"Task: {task}\nResult: {result}")

conn.close()

This is a simplified example, but it illustrates the core principle. Each agent would have a slightly modified prompt tailored to its specific role. For instance, the Fact Checker agent would receive a prompt emphasizing the need for accurate verification. The Web Scraper agent would use a prompt designed to generate search queries.

Challenges & Optimizations

This wasn't without its challenges.

Performance: TinyLlama, while impressive, is still slow on a phone. I optimized by limiting the context window (the amount of text the model considers at once) and using batch processing where possible.
Memory Management: SQLite is efficient, but the database can still grow quickly. I implemented a “garbage collection” system to periodically remove old or irrelevant data.
Prompt Engineering: Getting the agents to cooperate effectively required a lot of experimentation with prompts. Clear, concise prompts are crucial for guiding the LLM's behavior.
UI Responsiveness: Kivy wasn't the fastest UI framework, but it was the most straightforward option for quickly building a prototype.

Results & Future Directions

The current system is functional, albeit a bit sluggish. It can successfully summarize articles, answer questions, and generate