A practical and pragmatic discussion of the levels of risk and complexity in the customization of large language models. Many organizations are using LLM technology to build customized chatbots, RAG tools and content generators. However, many organizations do not have a full understanding of the options and levels of risk and development complexity that come from LLM customization and deployment.
In the contemporary landscape of artificial intelligence deployment, a structural shift is occurring: base models are becoming increasingly capable out of the box. Instruction-following performance, contextual reasoning, retrieval integration, and domain adaptability have improved to such a degree that many historical justifications for invasive model modification are steadily eroding. This evolution necessitates a corresponding philosophical and governance framework—one grounded in the principle that greater customization introduces greater uncertainty, greater liability, and a proportionally greater need for validation and risk controls.
At its core, the responsible deployment of large language models should be guided by a hierarchy of invasiveness. Each successive layer of intervention introduces deeper system coupling, increased behavioral unpredictability, and escalating regulatory, operational, and reputational risk. Accordingly, risk management should not begin at the level of model alteration, but rather at the least invasive layers of interaction and configuration.
Use of Base Frontier Models With Prompting Strategies:
The lowest-risk and most governance-aligned approach is the use of well-established base models accessed via controlled API interfaces. This strategy preserves the integrity of the original model, benefits from ongoing vendor maintenance, and minimizes the introduction of undocumented behavioral drift. In regulated or high-liability environments, this approach provides an auditable and stable foundation, as the model’s baseline characteristics are widely studied, documented, and continuously improved.
Within this initial layer, first-order prompting strategies represent the primary mechanism of customization. System prompts, role framing, response constraints, and structured output requirements allow substantial behavioral shaping without altering the model’s internal weights. This form of control is both reversible and transparent, making it highly favorable from a risk governance perspective. Critically, prompt engineering also enables iterative diagnostics, including diagnostic prompting techniques such as citation enforcement, reasoning transparency prompts, and controlled output formatting to better understand model behavior without modifying its internal structure.
An advanced method for prompting is to use chain of thought, pre-planning or multi step instructional prompts. In these circumstances, the model is provided with a “scratch pad” to allow for simulated cognitive reasoning and step-following of the model. Using methods that instruct the model to construct answers or check for errors in reasoning can create highly customized behaviors.
It is also possible to achieve levels of customization by gating models with filters on the input or output or by using scripting or algorithmic logic to format or filter inputs and outputs. Prompts may also be made dynamically, by task schedulers or other semi-automated interfaces. For public-facing LLMS, it is always necessary to have some level of gating and checking of inputs and outputs, as models can sometimes behave in unexpected ways and the output of a public facing model produces obvious liability and reputational concerns.
Retrieval Augmented Generation:
The next level of intervention involves Retrieval-Augmented Generation (RAG), which introduces external knowledge grounding while preserving the core model’s architecture. RAG is particularly attractive because it localizes customization to the data interface layer rather than the model itself. However, the risk profile increases if the retrieval pipeline is poorly optimized. Therefore, responsible deployment requires careful validation of data sources, indexing strategies, document chunking methods, and retrieval relevance metrics. Misaligned or poorly formatted retrieval sources are a common root cause of hallucinations and inconsistent outputs, and these failures often originate not from the model but from the knowledge interface.
Beyond baseline RAG implementation lies RAG optimization. This includes pre-vectorization, structured indexing, knowledge mapping, embedding optimization, and the integration of vector databases. These strategies enhance performance and consistency while still avoiding direct modification of model weights. Additional tool-calling frameworks, structured middleware layers, and modular orchestration systems can further refine outputs through controlled system design rather than invasive retraining. Importantly, these methods maintain reversibility and auditability, both of which are essential for enterprise risk control.
Subsequent escalation may involve model selection optimization. In many cases, performance deficiencies stem not from a flawed configuration but from a mismatch between the chosen base model and the task domain. Selecting a model optimized for instruction-following, retrieval integration, code generation, or domain reasoning can yield substantial gains without introducing the risks associated with weight modification. This stage reflects a fundamental governance insight: model replacement is often safer than model alteration.
MCP And Tool Using:
Beyond the use of external data sources, models can also be interfaced with tools. This can include SQL database search engines, calculators, code sandboxes and connection to outside services, like ecommerce platforms, financial systems and other sources of dynamic data, computation, search and other functions. It’s possible to build complex scaffoldings that allow the model to interact with a variety of systems.
A major innovation in the interface of models with other systems is MCP or Model Context Protocol. It bypasses much of the need for customized API tooling by creating a generic framework for how models can connect to outside protocols and services. MCP servers can be used as a bridge interface to outside services and to host a variety of custom tools.
Models may sometimes call on other AI models, such as second instances of LLMS or specialized LLM models to provide additional data parsing, lookup or verification. There are also circumstances where LLMs can be interfaced with task scheduler tools and task sandboxes, allowing more dynamic and sequential logical reasoning.
Models are capable of ingesting an increasingly wide types of data. Base models now have multimodal capabilities, which opens new capabilities for processing inputs and for interfacing with content generation. Models can ingest data as plain English instructions, JSON formatted data, structured data lists, knowledge maps, vector embeddings and other formats. Combining MCP with the models ability to process increasingly diverse input data allows for numerous customizations.
Beyond simply using tools, some organizations have developed entirely new tool sets and environments to allow for LLM capability expansion. MCP has made it easier to connect models to outside sources of data, but new search engines and database interfaces can aid in providing the model with the capabilities it needs.
For regulated environments and high risk outputs, the use of tools offers a greater level of control. Mathematical logic is more reliably computer by having the model offload arithmetic to a calculator module and, similarly, many algorithmic and fact lookup tasks may be better accomplished by external tools.
An increasingly important consideration is model output verification. Large language models can sometimes behave in unexpected ways, and are known to hallucinate or create strange narratives. In high risk environments, the models output should never be directly trusted for decision making of consequence. However, external tools and model calls can be used to check the output for unapproved content. In the case of code generation, sandboxes can be used to verify the validity of the code.
Agent Strategies:
The term for LLM agent has become slightly confused due its use to describe customized LLM chat instances. True AI agents are processes which can complete sequential step tasks with some level of autonomy. Agents are becoming a major area of interest in the use of LLMS. Some typical use cases for agents include surveying systems for security data, summarizing and responding to e-mails or updating websites based on new events.
In the simplest form, agents can be created from verbal descriptions, and platforms like Copilot seek to make this easy. However, more capable agents can be created with advanced features and behaviors expressed in more formal development frameworks. Agents can be customized to use tools or RAG and RAG and tool calling agents are becoming more common as strategies.
Complex tasks can be accomplished by multi agent strategies, such as task division and verification. Multi agent orchestration strategies can be accomplished using MCP, external tooling and A2A protocol, a protocol used to allow agents to communicate with other agents. A powerful impact of this is the ability for different providers and platforms to interface with each other.
It should be noted that agents introduce new risks in AI deployment. Agents remove the “human in the loop” inherent to chatbot-style deployments. Because models can hallucinate or may degenerate into strange and incoherent logical paths, it’s especially important to understand the limitations and failure impacts of an agent malfunction. At present, fully automated AI processes should not be trusted to make irreversible high impact decisions without human oversight and verification.
Agents and multi-agent strategies, when tasked with RAG-based tasks, tool use or automated tasks can be used to achieve very complex and sophisticated results. Importantly, they do not involve modifications to the base model.
Choosing an Alternative Base Model:
For most tasks, OpenAI, Anthropic and Google offer excellent models with a vast array of capabilities. Anyone who has used Claud or ChatGPT can attest to its ability to follow extremely complex instructions. For many verbal and subjective tasks, these models are simply unbeatable. They are also cost effective. API plans are available and the capabilities per token from a frontier lab model come at a far lower cost than alternatives, such as self-hosted models on cloud GPU’s.
However, there are many instances where organizations desire a higher degree of customization than can be achieved by frontier lab models and API calls. There are also data privacy and control issues and regulatory compliance reasons for self-hosting of models. Many organizations see self-hosting and choosing models to be an opportunity to innovate.
To this end, it’s important to understand that there are a large number of open source models available, and which one is chosen depends on a variety of factors. The landscape of models available continues to grow in both number and capability. Benchmarks are available that help understand the capabilities and comparisons between models. Important considerations, when choosing a base model include its context window size and whether it has been optimized for any given tasks.
The model landscape includes models that are especially well tuned for coding, RAG tasks, long context windows, creative writing, document transformation, translation, fictional personas and almost any other task one can imagine. Models exist as discrete models, checkpoints and even LORA adapter layers to existing models. It is important that the model chosen be well suited to the task. In fact, this can make an enormous difference. Using a RAG opsonized model versus using a model that is poorly trained in RAG can result in huge performance and accuracy differences.
Models like Deepseek, Qwen, Minimax, and the Llama family of large language models offer impressive capabilities, including simulated reasoning and tool use. However, an important consideration also must be model size and cost of hosting. Smaller models, such as Llama 7B offer basic capabilities and linguistic processing, but do not have the extensive knowledge and extended reasoning that comes from larger models. They may offer the benefit of more constrained outputs in certain circumstances. Moving to larger models offers more extensive and advanced capabilities, but at a cost. Small models may be locally hosted or hosted on cloud GPU infrastructure at a reasonable cost, often a dollar or two per hour per instance. However, as models scale, so too do requirements. Beyond 7b scale models, open source models can scale to more than 70B. But these models may require upwards of one hundred dollars per hour per instance for cloud GPU hosting and are often impractical for local hosting.
Choosing an alternative or self-hosted model can open up a great deal of new possibilities and risks. From a purely risk management standpoint, the most mainstream and industry standard base models offer the greatest confidence, while customized checkpoints and smaller projects may be higher in perceived liability.
Model Customization With SFT:
Modifying the weights of an established large language model offers the next level of customization. Some organizations tend to jump to this level of customization for model deployment, but that is increasingly unnecessary to achieve highly customized LLM behavior. Instead, this should be seen as an order of magnitude more complex in the deployment of custom NLP systems.
Only after exhausting non-invasive strategies should organizations consider fine-tuning through supervised methods such as Supervised Fine-Tuning (SFT). At this stage, the risk profile increases significantly. Fine-tuning introduces the possibility of catastrophic forgetting, behavioral drift, and unintended shifts in tone, reasoning patterns, or domain generalization. Consequently, data hygiene becomes a central risk factor. Fine-tuning datasets must be meticulously curated, diverse, well-constrained, and aligned with the intended behavioral direction of the model. Documentation of dataset provenance, annotation standards, and training objectives is essential for future auditability and governance review.
As with other types of customization, choosing the correct base model is key. For custom fine tuned models, it is necessary to assure that the base model is well suited to the task. Smaller models will generally be easier to fine tune to specific tasks and are more predictable in output. However, smaller models may be less plastic in their ability to integrate fine tuning into capabilities. Fine tuning is also expensive, from a GPU standpoint. It’s therefore important to make sure that the model is well chosen and the dataset well optimized.
That said, the cost is confined to the number of training sessions, and for large organizations, this may not be a major concern, as long as the sessions are kept to a minimum. Moreover, each fine tuning session adds to the risks of new problems arising. This can include such things as behavioral drift, capability reduction and catastrophic forgetting. These risks are minimized by reducing the number of sessions.
Following conventional fine-tuning, more advanced customization techniques such as knowledge distillation and preference tuning introduce additional layers of behavioral influence. These methods further shape model outputs but also increase opacity in causal attribution. As intervention depth increases, so too must the extensivity of evaluation protocols. Automated benchmarking across multiple prompt formats, adversarial testing, red teaming, regression analysis, and longitudinal behavioral tracking become necessary to detect drift and unintended side effects.
Because of the costs and risks of fine tuning, this may be considered a “measure twice, cut once” situation. Many organizations simply do not put enough effort into properly optimizing their data sets to best practices for fine tuning and resort to multiple sessions to achieve results which could have been more easily achieved in a single session.
Model weight modification can result in unpredictable changes in model behavior. It is also possible for models to experience “catastrophic forgetting” as well as tone drift or other capabilities degradation. It’s also possible that a fine tuning session did not fully achieve the changes in model output desired. For these reasons, it’s necessary to test and validate the model before release to production.
Testing can be highly automated and is often accomplished through the use of another LLM interface. It’s also possible to benchmark by heuristics or known knowledge verification. Such methods scale well. However, in some circumstances, only human evaluation can truly determine if a subjective behavior is valid.
One way to reduce the risk of catastrophic forgetting and behavior drift is to fine tune a LoRA (Low-Rank Adaptation) adapter. These training layers add new parameters to base models, without modifying the underlying strata. This makes them both more confined in their changes to the model and reversible, should they cause undesirable effects.
Reinforcement Learning:
Reinforcement Learning is the next level of customization for LLM deployment. This level of customization is necessary when model tone or behavior must be steered. It’s also an important part of building limits and guardrails into models and into preventing certain model behavior. For public facing models, RLHF can offer some additional safeguards against adversarial attack, but must be used with great care because of potential behavioral drift.
Reinforcement learning evaluates the models output and reinforces desirable outputs while suppressing undesirable outputs. Many forms of reinforcement learning can be automated and scaled to a large number of outputs, making them the most economical and scalable option. Reinforcement Learning with AI Feedback (RLAI) is an increasingly used technique and can be accomplished economically using frontier model API access.
Other, more traditional forms of automated feedback reinforcement learning are based on known knowledge, task accomplishment or heuristics. These methods have the advantage of being highly objective and reliable at scale. From a risk standpoint, such methods are desirable due to their greater auditability and predictability.
Reinforcement Learning from Human Feedback (RLHF) represents a particularly invasive and high-risk customization layer. While powerful, RLHF is inherently labor intensive and therefore expensive to deploy at scale. RLHF is necessary when truly subjective and human-centered evaluation is necessary. In these cases, however, it is important that carefully crafted instructions and metrics are made for human evaluators, so that the behavior of the model can be steered in the most predictable manner. If left to their own subjective judgement on model output, it is possible that misaligned behavior can be reinforced.
It should be noted that the need for human labor inherent to RLHF is a non-trivial barrier for many organizations. While it’s relatively easy to scale up things like compute and data, the logistics of labor intensive tasks are inherently difficult. To begin with, effective RLHF sessions range in the area of a few hundred to a few thousand man-hours at minimum. RLHF sessions. This represents a difficult to surmount barrier, since most organizations lack the staffing to accomplish this task, and using developers and engineers would be an ineffective use of resources.
From a practical standpoint, organizations have surprisingly few good options for this. Hiring temps is possible, but the amount of labor necessary is hard to justify even temp contract hiring. Moreover, contract hiring and temporary employees bring in additional overhead and onboarding costs. It represents a major step in approval for developers to ask for temporary hires to facilitate RLHF and makes sense for only the largest organizations.
Organizations sometimes turn to companies like Deloitte, KPMG and Accenture to provide for staffing of general company functions and projects. This is often the first place that companies turn for elastic capabilities, but it represents a high bar to cross. The costs of projects from major corporate contractors, like the big four, starts in the hundreds of thousands of dollars. At scale, the cost effectiveness is poor, as specialists cost hundreds of dollars for what is decidedly low skill work.
There are a variety of other contract options. Some firms, such as Robert Half offer staffing-centric contracting models and other organizations offer access to offshore workforces. Options may be increasing as custom RLHF becomes more popular. One thing, however, will not change, and that is that human labor for the purpose of RLHF will continue to be the most expensive and least scalable method of LLM customization.
As with any form of model modification, reinforcement learning may result in unpredictable downstream changes in model output. This can include misalignment and tonal drift. It is therefore necessary to extensively reevaluate a models output before releasing the model to production. The same measures used in fine tuning, such as automated and manual review of outputs is necessary. And, as with other aspects of model review, some of the work is difficult to fully automate.
One frustratingly common phenomena is tonal drift. The way a model words things, how it reacts to instructions and its overall tone and cadence is highly subjective. It is hard to quantify if the model seems “friendly” or “helpful” or “cold.” End users have become strongly attached to the perceived persona of models, and perception can change dramatically when tone shifts or perception of personality shifts.
Mechanistic Interventions:
Added for completeness, the most invasive and bleeding edge of model interventions are mechanistic interventions. These methods seek to change the models behavior, amend knowledge, change fact recall or add guardrails through direct interventions into the models parameters, bypassing the need for gradient decent based updates. In principle, such methods may offer more precise and limited ways of changing the model.
Exist methods include vector steering, embedding layer interventions, enforced sparsity, and, at the very bleeding edge, techniques which attempt to change the models knowledge or output directly. These include ROME (Rank One Model Editing) and MEMIT (Mass Editing Memory In Transformer), both of which attempt to find the locations where factual knowledge is located and modify it directly.
As with more conventional methods, model editing can cause model behavior changes that are difficult to predict. However, model editing is a far less well understood area of intervention and may not always achieve the desired impacts. These methods should be considered experimental.
The most compelling argument for mechanistic interventions is the need for hard guardrails and to assure that the model does not contain undesirable knowledge or behavior paths. The area of mechanistic interoperability is continuing to make progress in understanding how knowledge may be localized and models may be inspected for behavior, but much is still unknown.
One problem inherent to mechanistic intervention is that models do not store information in one place nor does prediction rely on one logical path, so it’s never possible to be sure that the necessary information was edited and other information not edited, in different contexts.
Development of New Base Models:
While it may seem overkill to develop an entirely new Large Language Model from the ground up, for some organizations and use cases, the option of creating a new LLM from the ground up is a viable option. Importantly, language models built for a narrow task or use case do not need to be anywhere near as large as typical base models.
The possibility of developing a new model from the ground up has become more accessible in recent years. Open source frameworks and source code are available and preformatted corpora of text is also available. Automated model production pipelines have also come into existence. However, it should be noted that this will require an irreducibly large amount of GPU compute and is inherently expensive
Base model creation is also expensive in other ways. Corpora curation, evaluation, preference tuning and reinforcement tuning all are extremely labor intensive. Depending on the use case for the model, it may be necessary to find specialized data sets or have custom labeling on existing data.
There are a number of reasons that organizations may do this, despite the cost. For large organizations, having full control of a proprietary tool is appealing, and this can only be accomplished by owning the entire model pipeline. There are also narrow use cases where custom models suffice. Custom models are most common in research and development settings. In other cases, organizations may see a custom LLM as an opportunity for innovation and leadership.
What we have seen, in recent years, is some amount of custom model development in various organizations adjacent to the primary frontier labs. Companies like Meta, Microsoft, NVidia and Scale.ai, have all begun to develop their own proprietary inhouse models. It’s not unlikely that other large tech-centered companies will see it as worthwhile to develop their own base models.
There is also a thriving open source community. New models, new types of models and the components necessary to build and train models are available from sources like HuggingFace. The rate of innovation in model development has been staggering.
Development of New Model Architectures:
At the very end of the spectrum of intensiveness for model customization is the use of new, experimental and novel model architectures. This is the domain of only a few organizations, but it is certainly a real and substantive area of research. New and alternative model designs include state space models, multi token prediction, diffusion language modeling and potential custom implementation, such as models that combine state space and transformer architectures.
There certainly is a place for the development and experimentation with new model types, new architectures and novel ways of processing language. This is very much an active area of research. It is mostly found in academia, where near model types are commonly the result of cutting edge research. There is also a surprising amount of forward-thinking research in the amateur and open source community. Large organizations like OpenAI, Anthropic and Google are constantly researching new model types and tweaks to existing models.
It is important to note that substantive progress can be made in custom models and new model types by organizations far smaller than foundation model creators. Models do not need to be anywhere near the scale of foundation models to be useful in narrow domains or to demonstrate an advantage over current methods and architectures.
The risks of developing a new model or a new architecture are difficult to quantify, since the area is so broad and the risks are bound by use case. It goes without saying that new base models require the greatest of auditing and quality control testing.
Conclusion:
In summary, the responsible deployment of large language models should follow a principled hierarchy of intervention: beginning with base model utilization and prompt-level control, progressing through retrieval integration and system optimization, and only advancing toward fine-tuning, reinforcement learning, or architectural redesign when lower-risk methods are demonstrably insufficient. Each successive layer increases customization, but also amplifies uncertainty, validation burden, and governance responsibility. As base models continue to improve, the strategic and regulatory logic increasingly favors minimal invasiveness, rigorous documentation, and layered risk controls rather than premature model modification. This philosophy aligns not only with operational efficiency, but with long-term safety, auditability, and organizational resilience in high-stakes AI deployment environments.