Pushing the Boundaries: Advanced Techniques for Production LLM & RAG Systems¶
This article outlines advanced architectures and techniques for production and enterprise-scale AI systems, exploring cutting-edge model optimisation, sophisticated retrieval strategies, complex reasoning frameworks and robust security considerations that push the boundaries of what's possible with LLM and RAG implementations.
These are more descriptions of other LLM and RAG terminology within my basic LLM and RAG terminology article and my intermediate practitioner article.
Model Architecture & Fine-Tuning¶
Parameter Efficient Fine-Tuning (PEFT) Sophisticated methods to customise models using minimal computational resources while preserving most of the pre-trained model's capabilities. These techniques significantly reduce the memory and computation requirements for adaptation, making fine-tuning accessible with consumer hardware.
LoRA/QLoRA (Low-Rank Adaptation) A technique that adds small trainable matrices to frozen model weights, reducing fine-tuning costs by over 90% while maintaining quality. LoRA works by decomposing weight updates into low-rank matrices that capture essential adaptation patterns without modifying the original model parameters. QLoRA extends this by quantizing the base model to further reduce memory requirements.
DoRA/QDoRA (Double Rank Adaptation) An enhancement to LoRA that uses both multiplicative and additive updates, often achieving better performance for domain adaptation. DoRA addresses limitations in traditional fine-tuning approaches by providing more flexible weight adjustments, allowing for more nuanced adaptation to specific domains or tasks while maintaining the efficiency benefits of low-rank methods.
Mixture of Experts (MoE) An architecture where specialised sub-models handle different types of queries, dramatically improving efficiency for large models. MoE systems only activate relevant "expert" networks for each input, enabling scaling to trillions of parameters while reducing computational requirements. Whilst this approach requires considerable engineering complexity for production systems, it allows models to develop specialised capabilities for different domains or reasoning types without requiring all parameters to be used for every inference.
Latent Knowledge Extraction Techniques to access information embedded within model weights that isn't explicitly surfaced through standard prompting. These methods treat the model itself as a knowledge base, using specialised prompting or activation analysis to uncover facts and relationships the model has learned but doesn't readily produce through conventional interaction patterns.
Weight Orthogonalization A permanent modification to model weights that removes specific capabilities by identifying and neutralising certain activation directions. This technique allows for targeted removal of unwanted behaviors or knowledge while preserving desired functionality, creating more controlled and predictable models for specialised applications.
Advanced RAG Architectures¶
Graph RAG
An advanced RAG architecture that uses structured knowledge graphs rather than simple vector similarity to enhance retrieval and reasoning capabilities. Unlike traditional RAG that treats documents as independent chunks, Graph RAG extracts entities and relationships from source materials to build a comprehensive knowledge graph, then organises these into hierarchical semantic clusters. This approach excels at two critical challenges where traditional RAG often fails: connecting information across disparate sources through relationship traversal, and providing holistic understanding of themes and patterns across entire document collections.
The implementation process involves extracting entities and relationships from source documents, building a structured knowledge graph, creating semantic clusters with pre-generated summaries, and using both the graph structure and clusters during retrieval. When a query is received, Graph RAG identifies relevant entities, traverses their relationships to find connected information, and assembles context that includes both specific facts and broader thematic understanding.
This architecture significantly outperforms traditional RAG for complex questions requiring multi-hop reasoning or dataset-wide analysis, while maintaining similar levels of factual accuracy and providing clear provenance back to source materials. Graph RAG represents a significant evolution in RAG systems, moving from simple similarity matching toward structured knowledge representation that better mimics human understanding of complex information landscapes.
This diagram shows how user queries trigger both knowledge graph traversal and vector search, with the results combined to provide comprehensive context for the LLM's response.
This approach is particularly valuable for domains with complex, interconnected information such as scientific research, legal analysis, financial compliance, and enterprise knowledge management, where understanding relationships between entities is as important as the entities themselves.
RAFT (Retrieval-Augmented Fine-Tuning) A hybrid approach that combines the benefits of RAG and fine-tuning to create more specialised and accurate models. Unlike standard RAG which retrieves information at inference time, RAFT incorporates domain-specific knowledge during the fine-tuning process itself. This technique first retrieves relevant documents for each training example, then uses this augmented context when fine-tuning the model, effectively teaching it to better understand and utilise domain-specific information. The resulting model develops enhanced capabilities for the target domain while requiring less retrieval during inference, often improving response quality, reducing latency, and decreasing operational costs. RAFT is particularly valuable for specialised applications where consistent domain expertise is required.
Agentic RAG An advanced architecture that combines RAG with autonomous agent capabilities, enabling more sophisticated information-seeking behavior. Unlike traditional RAG that performs a single retrieval step, Agentic RAG implements a dynamic, multi-step process where the system can formulate follow-up queries, explore different information paths, and synthesize findings across multiple retrievals. The agent component makes decisions about what additional information to seek, when to refine search strategies, and how to combine information from multiple sources. This approach is particularly effective for complex queries requiring multi-hop reasoning, research-oriented tasks, or situations where the initial retrieval might be insufficient to fully address the query.
Hypothetical Document Embeddings (HyDE) Creating imaginary "perfect" documents that would answer a query, then using these to search real documents. This innovative approach leverages the LLM's ability to imagine ideal responses, then uses these hypothetical documents as search proxies, often retrieving more relevant results than direct semantic search, particularly for complex or hypothetical queries.
Advanced retrieval optimisation¶
Cross-Encoder Reranking Model Fine-Tuning A specialised adaptation technique that optimises reranking models for domain-specific relevance assessment. Unlike general-purpose rerankers that evaluate query-document relevance based on broad web data, fine-tuned cross-encoders are trained on domain-specific relevance judgments, dramatically improving precision for specialised applications. The process typically involves creating a dataset of query-document pairs with human-annotated relevance scores from the target domain, then fine-tuning pre-trained cross-encoder models like those from MS MARCO or BERT-based rerankers. This approach significantly enhances retrieval quality for domain-specific terminology, industry-specific relevance criteria, and specialised information needs that general models might misinterpret. Organisations implementing this technique often see substantial improvements in retrieval precision, particularly for queries containing domain-specific terminology or concepts.
Embedding Model Fine-Tuning A technique for adapting general-purpose text embedding models to better capture semantic relationships within specific domains or for particular retrieval tasks. While pre-trained embedding models like those from OpenAI or HuggingFace provide strong general-purpose performance, they often miss nuanced semantic distinctions in specialised fields. Fine-tuning these models on domain-specific text pairs with known similarity relationships creates embeddings that better reflect the semantic space of the target domain. Implementation approaches include contrastive learning with domain-specific positive and negative examples, supervised fine-tuning with labeled similarity pairs, or distillation from larger domain-adapted models. The resulting embeddings demonstrate improved clustering of related concepts, more accurate similarity assessments, and ultimately better retrieval performance for domain-specific applications.
Retrieval Evaluation Metrics¶
Specialised measurements used to assess the quality of retrieval and ranking systems, essential for evaluating and fine-tuning components like cross-encoder rerankers. Unlike simple accuracy metrics, these measures account for the position of relevant items in search results, reflecting the real-world importance of returning the most relevant information first. By optimising models against these sophisticated measurements rather than basic accuracy, organisations can create retrieval systems that better match user expectations for search quality and relevance ordering, ultimately delivering more effective information access.
Position-aware metrics¶
NDCG (Normalized Discounted Cumulative Gain) A comprehensive ranking quality metric that evaluates how well a retrieval system places the most relevant documents at the top of search results. NDCG calculates the cumulative gain of retrieved documents, applying a discount factor that reduces the weight of documents appearing lower in results, then normalizes this against an ideal ordering. This normalisation allows for comparison across queries with different numbers of relevant documents. NDCG ranges from 0 to 1, with 1 representing perfect ranking where the most relevant documents appear first. NDCG specifically uses a logarithmic discount function (typically log2(i+1) where i is the position) to penalize lower-ranked items. This specific discount function is what distinguishes it from other cumulative gain metrics.
NDCG@k A variant of NDCG that evaluates only the top k results (commonly NDCG@10), reflecting the reality that users typically focus on the first page of search results. By limiting evaluation to the most visible results, NDCG@k provides a more practical assessment of user experience and is less computationally expensive for large document collections. This metric is particularly valuable when fine-tuning reranking models, as it focuses optimisation efforts on improving the most visible results.
MRR (Mean Reciprocal Rank) A metric that focuses specifically on the position of the first relevant result, calculated as the average of the reciprocal of that position across multiple queries (1 for first position, ½ for second, etc.). Unlike NDCG, which evaluates the entire ranking, MRR emphasizes finding at least one correct answer quickly — making it particularly suitable for question-answering systems or scenarios where users need just one correct result. When fine-tuning reranking models for precision-critical applications, MRR often serves as a primary optimisation target.
Set-based metrics¶
While Precision@k and Recall@k are position-aware in that they focus on the top-k results, these metrics treat all positions within that set equally, unlike NDCG which differentiates between positions.
Precision@k A metric that measures the proportion of relevant documents among the top k retrieved results. Unlike NDCG, Precision@k treats all relevant documents equally without considering their degree of relevance or exact position within the top k. This straightforward metric is particularly valuable for applications where binary relevance (relevant/not relevant) is sufficient, such as filtering systems or compliance searches where any relevant document must be found.
Recall@k A measure of how many of the total relevant documents in the collection are successfully retrieved within the top k results. While precision focuses on result quality, recall measures completeness — how many relevant items were missed. This metric is critical for applications where finding all relevant information is essential, such as legal discovery, medical research, or comprehensive literature reviews.
Composite metrics¶
MAP (Mean Average Precision) A comprehensive metric that combines precision and recall considerations across multiple relevance thresholds. MAP calculates the average precision at each point where a relevant document is retrieved, then averages these values across all queries. This provides a single-figure measurement that rewards retrieving relevant documents earlier in the results while also considering overall recall. MAP is particularly valuable for evaluating systems where both precision and recall matter.
Reciprocal Rank Fusion (RRF) Previously introduced as a fusion algorithm for combining search results, RRF is primarily used to merge results from different retrieval systems rather than as an evaluation metric itself. When evaluating retrieval systems, RRF provides a way to assess how effectively a system combines results from multiple retrieval approaches. This algorithm is particularly relevant when evaluating hybrid retrieval systems that use different strategies for different query types or that combine dense and sparse retrieval methods.
F1 Score The balanced F1 score, where precision and recall are weighted equally. This is the harmonic mean of precision and recall, providing a balanced measurement that penalizes systems that sacrifice one metric for the other. This is especially useful when neither precision nor recall alone adequately captures system requirements, such as in medical information retrieval where both finding all relevant information (recall) and avoiding irrelevant information (precision) are important. There are also weighted variants like F2 (emphasizing recall) and F0.5 (emphasizing precision) for applications where one aspect is more critical than the other.
Extended approaches¶
Relevance Probability (NDCG variant) A probabilistic interpretation of relevance that accounts for uncertainty in relevance judgments. Unlike traditional NDCG which assumes perfect knowledge of relevance, this approach incorporates the probability that a document is relevant, making it more robust when working with incomplete or noisy relevance judgments from multiple annotators.
Advanced Retrieval Strategies¶
Self-RAG Systems that can decide when to retrieve information versus using existing knowledge, dynamically determining if external information is needed. Self-RAG models incorporate retrieval decisions into their generation process, evaluating when to trust their parametric knowledge and when to seek external verification, creating more reliable and efficient information access.
Recursive Retrieval Multi-step retrieval processes that build on initial results, using information from first-round retrieval to guide subsequent, more focused searches. This iterative approach allows systems to progressively refine their understanding and retrieval strategy, starting with broad context and narrowing to specific details through multiple retrieval steps.
Adaptive Retrieval Systems that adjust search strategies based on query types, automatically selecting between vector, keyword, or hybrid approaches depending on the nature of the question. This intelligence allows RAG systems to optimise for different information needs - using semantic search for conceptual questions, keyword search for specific facts, and hybrid approaches for complex queries requiring both.
Query-by-Example Finding similar content by providing examples rather than descriptions, particularly useful for complex or nuanced information needs. This approach allows users to search by showing rather than telling, enabling retrieval based on patterns that might be difficult to articulate explicitly but are present in example documents.
Dense Passage Retrieval specialised techniques for retrieving relevant text passages using bi-encoders trained specifically for retrieval tasks. These models are optimised to create embeddings that prioritise retrieval performance rather than general semantic understanding, often incorporating contrastive learning approaches to maximise the separation between relevant and irrelevant content.
Binary Hashing for Approximate Search / Quantization-Based Pre-filtering A two-stage retrieval optimisation technique that first converts high-dimensional embeddings into compact binary representations (hashes) for rapid preliminary filtering, followed by precise reranking using the original full-precision embeddings. The binary quantization drastically reduces memory requirements and computation costs during the initial broad search, allowing systems to efficiently scan millions of documents in milliseconds. After identifying a promising subset of candidates using these binary hashes, the system applies a more computationally intensive reranking step using the original high-dimensional embeddings to precisely order results by relevance.
Common implementations include Locality-Sensitive Hashing (LSH), Product Quantization (PQ), and Scalar Quantization (SQ), each offering different trade-offs between speed, memory efficiency, and retrieval quality. This approach is particularly valuable for large-scale retrieval systems where the embedding collection is too large to perform exhaustive similarity search efficiently.
Advanced Vector Search Considerations¶
ANN Pre-filtering Problem The significant performance degradation that occurs when applying metadata filters before vector search, often forcing fallback to slower exact search methods. This fundamental limitation of approximate nearest neighbor algorithms requires careful system design to balance filtering needs with retrieval performance, often necessitating specialised indexing strategies or hybrid retrieval approaches.
Multi-Vector Representations Using multiple specialised embeddings for different aspects of documents to improve retrieval performance. This approach includes title vectors optimised for short text matching, content vectors for detailed information retrieval, specialised embeddings for technical content that capture domain-specific meanings, and hybrid representation strategies that combine multiple embedding types for comprehensive document representation.
Vector Database Scaling Techniques for managing vector search at massive scale, including sharding strategies that distribute vectors across multiple nodes, distributed vector indices that enable parallel search, quantization optimisation that reduces memory requirements while preserving search quality, filter-aware indexing that improves filtered search performance, and hierarchical clustering that enables efficient navigation of large vector spaces.
Vector Database Orchestration Advanced techniques for managing vector data across multiple databases or indexes to optimise for different retrieval scenarios. This includes strategies like:
- Tiered retrieval systems using different vector databases for different stages
- Multi-index architectures that maintain specialised embeddings for different query types
- Hybrid orchestration layers that intelligently route queries to appropriate vector stores
- Federated vector search across distributed or specialised indexes
- Dynamic replication and sharding strategies for high-availability enterprise deployments
Advanced Reasoning Frameworks¶
Chain-of-Thought (CoT) Guiding models through step-by-step reasoning to solve complex problems, significantly improving accuracy for mathematical, logical, and multi-step tasks. By explicitly prompting models to show their work rather than jumping to conclusions, CoT unlocks more reliable problem-solving capabilities, particularly for questions requiring multiple inferential steps.
Tree-of-Thought (ToT) An extension of CoT that explores multiple reasoning paths simultaneously, evaluating different approaches before selecting the most promising solution. This framework enables models to consider alternative problem-solving strategies, backtrack from dead ends, and assess intermediate results, mimicking more sophisticated human reasoning processes for complex problems.
ReAct Framework A system combining reasoning and action, where the model alternates between thinking through a problem and taking actions to gather information. This approach creates a feedback loop between reasoning and information gathering, allowing models to dynamically decide what additional information they need and how to obtain it to solve complex tasks.
Reflexion A technique where models critique and refine their own outputs, creating a feedback loop that improves answer quality without human intervention. By generating self-criticism and iteratively improving responses based on this analysis, Reflexion enables models to catch their own errors and reasoning flaws, producing higher quality final outputs.
Advanced Evaluation Techniques¶
LLM as Judge Calibration Methods to ensure consistent and reliable evaluation when using LLMs to assess outputs. These approaches include establishing baseline judgments with human evaluators to anchor model assessments, measuring and correcting for systematic biases in model evaluations, and using multiple prompt variations to reduce variance in judgment. Proper calibration is essential for creating reliable automated evaluation systems.
Automated Red-Teaming Systematic approaches to stress-test AI systems by automatically generating challenging or adversarial inputs to identify weaknesses. These techniques create a continuous testing environment that probes for failure modes, safety issues, and performance limitations, enabling more robust system development and risk mitigation.
Faithfulness Metrics Sophisticated measurements of how accurately LLM outputs reflect the provided source materials, detecting fabrications or misrepresentations. These metrics quantify the degree to which generated content is grounded in and supported by the retrieved information, providing objective measures of hallucination and factual reliability.
ROUGE/BLEU/BERTScore specialised metrics for comparing generated text to references, using different approaches to measure similarity and quality. These evaluation methods range from n-gram overlap measures (ROUGE, BLEU) to semantic similarity assessments (BERTScore), each capturing different aspects of text quality and faithfulness to reference material.
Multimodal and Cross-Modal Systems¶
Model Context Protocol (MCP) A standardised framework for efficiently managing and optimising context in large language model interactions. MCP provides structured methods for organising, prioritising, and compressing information within the context window, enabling more effective use of limited context space. The protocol defines clear patterns for separating system instructions, conversation history, and retrieved information, while establishing guidelines for context pruning and maintenance across complex, multi-turn interactions. Implementing MCP helps organisations standardise their approach to context management across different models and applications.
Image-to-Text Retrieval Systems that can find relevant text based on image queries, bridging the gap between visual and textual information. These capabilities enable searching document collections using visual references, significantly expanding retrieval possibilities beyond text-only queries.
Cross-Modal Embeddings Representations that capture both visual and textual information in the same vector space, enabling unified search across different content types. These embeddings create a common mathematical space where semantically similar content appears close together regardless of modality, allowing for seamless integration of multimodal information.
Vision Language Models (VLMs) Advanced AI systems that integrate visual perception with language understanding, enabling sophisticated reasoning across both modalities. Unlike traditional LLMs, VLMs incorporate visual encoders that transform images into representations that can be processed alongside text, allowing the model to reason about what it "sees." Modern VLMs can perform complex tasks like visual question answering, detailed image description, object identification, spatial reasoning, and following instructions that reference visual content.
Implementation architectures typically involve specialised image encoders (often based on transformer or CNN architectures) coupled with language models, with various approaches to aligning the visual and textual representations. Leading VLMs increasingly support multi-image reasoning, visual grounding of language, and fine-grained understanding of diagrams, charts, and documents.
Multimodal RAG Systems that extend traditional RAG beyond text to incorporate diverse media types like images, audio, and video into the retrieval and generation process. While VLMs focus on understanding and reasoning about visual content, multimodal RAG specifically addresses how to retrieve and reference this content from external knowledge sources.
These systems enable applications where:
- Users can submit queries containing mixed media (text + images)
- The system retrieves the most relevant content regardless of media type
- Responses incorporate information synthesized across different modalities
- Evidence from various media types grounds the generation process
Implementation challenges include creating unified embedding spaces for cross-modal similarity search, developing effective ranking algorithms that work across media types, and determining how to present multimodal information within the context window. Advanced implementations may incorporate specialised indexes for different media types and modal-specific retrieval strategies while maintaining a unified retrieval framework.
Advanced Agent Architectures¶
Agent Orchestration Systems for coordinating multiple specialised agents to solve complex problems, often involving agent collaboration, role specialization, and hierarchical planning structures. These architectures enable teams of agents to work together on tasks requiring diverse expertise, with supervisor agents decomposing problems and delegating to specialised worker agents.
Agentic Memory Systems Advanced approaches for maintaining context and learning from experience across interactions. These systems go beyond simple conversation history to include episodic memory (specific experiences), semantic memory (general knowledge), and reflective processes that help agents improve through experience and avoid repeating mistakes.
Autonomous Agent Loops Self-directed systems that can operate continuously with minimal human supervision, incorporating planning, execution, observation, and reflection cycles. These agents can set their own goals, monitor their progress, adapt to changing conditions, and operate independently for extended periods on complex tasks.
LangGraph An extension of LangChain focused on building stateful, multi-actor applications using a graph-based architecture. While standard LLM frameworks excel at sequential workflows, LangGraph enables more complex interaction patterns where multiple components can operate with different responsibilities in flexible topologies. The framework provides structures for creating persistent state, defining transitions between components, and managing complex decision flows. LangGraph uses a directed graph model where nodes represent distinct processing steps or agents, and edges define the possible transitions between them, allowing for sophisticated control flow including loops, conditional branches, and parallel execution paths. This approach is particularly valuable for implementing advanced agent architectures, complex reasoning systems, and applications requiring iterative refinement or multi-step planning.
SmoLAgents A lightweight, modular framework for building LLM-powered agents that can accomplish complex tasks with minimal computational resources. Unlike traditional agent architectures that rely on large, resource-intensive models for all components, SmoLAgents strategically distributes responsibilities across specialised small language models (SLMs), significantly reducing computational requirements while maintaining high performance. The framework assigns different cognitive functions (planning, tool use, memory management) to purpose-tuned small models that work together through a coordinated message-passing system. This approach enables sophisticated agent capabilities on resource-constrained environments like edge devices or in applications where latency and cost considerations are paramount.
LlamaIndex Workflows A structured framework for orchestrating complex, multi-step LLM operations within the LlamaIndex ecosystem. Unlike basic agent implementations that follow fixed patterns, LlamaIndex Workflows enables the creation of flexible processing pipelines where different retrieval strategies, reasoning approaches, and validation steps can be combined and conditionally executed based on query characteristics or intermediate results. The system provides built-in components for common operations like query planning, sub-question decomposition, and result synthesis, while allowing for custom components and decision logic. This approach is particularly valuable for handling complex information needs that require adaptive processing strategies depending on the query type or the nature of the retrieved information.
Enterprise-Scale Implementation¶
Federated Deployment Distributing AI capabilities across multiple environments while maintaining centralised control, especially important for organisations with strict data sovereignty requirements. This approach allows enterprises to deploy LLM capabilities in multiple regions or security domains while maintaining consistent governance, model versions, and operational oversight.
Privacy-Preserving Techniques Methods to maintain data security in LLM systems while enabling effective functionality. These include differential privacy that adds controlled noise to protect individual data points, federated learning that trains models without centralising sensitive data, local inference that keeps data within secure environments, secure enclaves that provide hardware-level protection, and data minimisation strategies that limit exposure of sensitive information.
Metadata Enrichment The process of adding contextual information to document chunks at indexing time, enabling more precise filtering and better contextualization of content. This enhancement improves retrieval precision by capturing document attributes, relationships, and context that might not be apparent from the text alone, creating richer information access capabilities.
Hybrid Orchestration Frameworks for managing interactions between multiple models, retrieval systems, and tools. These orchestration layers determine when to use different models based on query type, confidence levels, or specialised needs, creating more robust and efficient systems.
Advanced Security Considerations¶
Prompt Injection Defenses Sophisticated techniques to prevent manipulation of AI systems through carefully crafted inputs. These protections include sandboxing that isolates execution environments, instruction reinforcement that repeatedly emphasises system guidelines, context boundary enforcement that prevents user inputs from being interpreted as system instructions, input sanitisation that removes potentially malicious content, and adversarial training that improves resistance to manipulation attempts.
Jailbreak Resistance Methods to prevent bypassing of AI safety measures and guardrails. These approaches include robust alignment techniques that deeply encode safety values, safety layering that implements multiple complementary protections, response verification that checks outputs before delivery, continuous monitoring that detects exploitation attempts, and adaptive defenses that evolve in response to new attack vectors.
Threat Priming Mitigation Techniques to resist manipulation through threats or coercion, ensuring consistent safety boundaries regardless of user approach. These specialised defenses protect against attempts to intimidate or pressure the model into producing harmful content by maintaining safety guardrails even under adversarial conditions.
Cutting-Edge Research Areas¶
Attention Mechanism optimisation Advanced improvements to the core transformer attention process that enhance efficiency and capability. These innovations include sparse attention patterns that focus computation on the most relevant tokens, linear attention mechanisms that reduce computational complexity, sliding window attention that efficiently processes long sequences, and state space models that offer alternatives to traditional attention mechanisms.
Synthetic Data Generation Creating artificial training or evaluation data for improving model capabilities. Approaches include adversarial generation that creates challenging examples, data augmentation that expands limited datasets, counterfactual examples that help models understand causal relationships, and distribution matching that ensures synthetic data maintains the statistical properties of real data.
Knowledge Graph Integration Combining structured knowledge representations with neural approaches to enhance reasoning and factual reliability. These techniques include entity linking that connects text mentions to knowledge base entries, relationship extraction that identifies connections between entities, graph-enhanced retrieval that leverages structured relationships for better information access, and structured reasoning that combines neural and symbolic approaches for more reliable inference.
Graph RAG is a specific implementation approach that applies knowledge graph integration principles to RAG systems.
Chris Thomas is an AI consultant helping organisations validate and implement practical AI solutions.