What does a 'good' AI implementation look like in production?

What does a 'good' AI implementation look like in production?

A good AI implementation is a system that delivers verifiable, low latency, and cost effective results while remaining maintainable by a standard engineering team. In our experience, many teams struggle to move beyond the experimental phase because they lack a concrete definition of success. To be considered production grade, a system must move past simple prompt engineering and into a structured architecture where data quality is governed, latency is strictly bounded, and every model response is logged for evaluation.

When we evaluate client systems, we look for four distinct pillars: performance reliability, architectural modularity, cost transparency, and rigorous monitoring. A system that succeeds in a laboratory setting but takes ten seconds to respond or costs $2.00 per query is not a production system; it is an expensive prototype. Our team defines a good implementation as one that hits a sensible P95 latency target for Retrieval Augmented Generation (RAG) applications -- often under a few seconds for interactive use cases, though the right target depends on how the feature is used.

The following table summarizes the shift from an experimental setup to a production grade implementation:

The 4 Pillar AI Maturity Framework

To help our clients navigate this transition, we use the 4 Pillar AI Maturity Framework. This framework allows a head of data or a senior engineer to audit their current stack and identify exactly where the gaps are.

1. Performance and Reliability

A system must be fast enough to be useful. If a user has to wait more than a few seconds for a response from an internal tool or a customer facing agent, the adoption rate will plummet. We prioritize P95 latency because it captures the experience of your most frustrated users. To hit the latency target you set for a given use case, we often implement semantic caching, prompt compression, and asynchronous processing for non critical tasks.

2. Architectural Modularity

A good implementation does not rely on a single massive Python script. Instead, it uses modular agentic workflows. This means separating the retrieval logic, the reasoning logic, and the tool execution. We often build these using dbt for the data transformation layer to ensure that the data being fed into the LLM is clean, deduplicated, and formatted correctly.

3. Cost Transparency and TCO

Total Cost of Ownership (TCO) is the metric that kills AI projects in the boardroom. A good implementation tracks token usage at the user level, allowing the team to calculate the ROI of specific features. Once monthly compute reaches a meaningful scale, the choice between custom logging and vendor tools becomes a critical financial decision.

4. Continuous Evaluation

Production AI is not a set and forget system. It requires a feedback loop where humans or "LLM as a judge" systems score the outputs. We recommend the AI Stack Audit for teams that need to baseline their current evaluation capabilities before scaling.

Enterprise LLM deployment best practices 2025

By 2025, the standard for enterprise LLM deployment has shifted from "can we build it?" to "can we maintain it?" Deployment is no longer just about the model weights; it is about the entire surrounding ecosystem. In our work with mid market SaaS companies, we have identified several best practices that separate the leaders from the laggards.

First, decouple your application logic from your specific model provider. A good implementation uses an abstraction layer (like LiteLLM or a custom gateway) that allows you to swap between Claude, GPT 4o, or a local Llama 3 instance without rewriting your core code. This protects you from model deprecations and price hikes.

Second, treat your prompts like code. This means using version control (Git), implementing a peer review process for prompt changes, and running automated regression tests. If a prompt change improves accuracy for one use case but causes the model to start hallucinating in another, your CI/CD pipeline should catch it before it hits production.

Third, leverage your existing Data Stack (MDS). If your team is already using BigQuery and dbt, do not build a separate, isolated silo for your AI data. Integrate your vector embeddings directly into your BigQuery environment or use dbt to prep your chunks. This ensures that your AI respects the same data governance and access controls as your BI reports.

-- Example dbt model for preparing LLM chunks
-- This ensures clean, governed data reaches the vector store
WITH raw_docs AS (
    SELECT 
        id,
        content,
        updated_at,
        metadata_json
    FROM {{ ref('stg_internal_docs') }}
    WHERE is_active = TRUE
),

cleaned_chunks AS (
    SELECT
        id,
        -- Remove HTML tags and normalize whitespace
        REGEXP_REPLACE(content, r'<[^>]*>', '') AS clean_content,
        metadata_json
    FROM raw_docs
)

SELECT 
    id,
    clean_content,
    metadata_json,
    CURRENT_TIMESTAMP() AS processed_at
FROM cleaned_chunks

Monitoring AI model performance in production

Monitoring AI model performance in production requires a different toolkit than standard software monitoring. While you still need to track 500 errors and CPU usage, you also need to track "semantic drift" and "hallucination rates."

We categorize monitoring into three layers:

1. Infrastructure Monitoring: Tracking API uptime, latency, and token throughput. This is the baseline for any technical system.
2. Behavioral Monitoring: Tracking how often users accept or reject the AI suggestions. This provides a proxy for real world utility.
3. Model Monitoring: Using automated evaluations to check for bias, toxicity, or accuracy against a "golden dataset" of known good answers.

When we deploy systems for our clients, we often help them decide between building custom logging into their existing SQL infrastructure or paying for a dedicated observability vendor. For a team spending $5,000 to $8,000 on a specific AI workflow, custom logging in BigQuery is often more cost effective than a $2,000 per month observability subscription. We teach these trade offs in our Learn AI Bootcamp, where we focus on building sustainable data foundations.

Local versus managed vector databases for enterprise scale

A frequent point of debate for data teams is whether to use a local vector store or a managed enterprise solution. For a production implementation, the choice depends on your security requirements and your scaling needs.

In our experience, teams should start with managed solutions unless they have a strict regulatory requirement to keep data on prem. The engineering overhead of managing a distributed vector database often outweighs the subscription costs.

AI production readiness checklist for data teams

Before you move any AI feature to a production environment, your team should be able to check off every item on this list. Failure to do so often leads to "AI debt," where the team spends more time fixing bugs than building new features.

• Latencies are measured and bounded: P95 meets the target you set for the end to end request.
• Data is governed by dbt: No raw, uncleaned data is being sent to the LLM.
• Evaluations are automated: At least 50 "golden" question and answer pairs are tested on every deployment.
• Token costs are attributed: You know exactly which user or department is driving compute costs.
• Fallback mechanisms exist: If the primary LLM API is down, the system provides a graceful error or switches to a secondary model.
• Secrets are managed: No API keys are visible in the codebase or the logs.
• Feedback loops are active: There is a "thumbs up" or "thumbs down" UI for users to report quality issues.

If you cannot check off at least five of these items, your implementation is likely still in the prototype phase. This is not necessarily a bad thing, but it means you should be cautious about rolling it out to a wide audience or using it for mission critical tasks.

Why modular agentic workflows beat simple wrappers

Most early AI projects start as simple wrappers around a GPT model. You take a user prompt, send it to the API, and return the result. While this works for demos, it fails in production because it is "brittle." If the user asks a question that requires data from three different sources, a simple wrapper will likely hallucinate or give an incomplete answer.

A "good" implementation uses a modular agentic workflow. This architecture breaks the task into steps:

1. Intent Classification: What is the user actually asking for?
2. Tool Selection: Does this require a SQL query, a vector search, or a call to the CRM API?
3. Execution: Run the necessary tools in parallel.
4. Synthesis: Combine the tool outputs into a coherent response.

This modularity allows for better debugging. If the final answer is wrong, you can see if the failure happened during the intent classification stage or if the vector search returned the wrong documents. It also allows you to use smaller, faster, and cheaper models for simple tasks (like intent classification) and save the expensive models for the final synthesis.

Frequently Asked Questions About AI Production

How much should we spend on LLM observability?

For most mid market data teams, we suggest spending no more than 10% to 15% of your total model spend on observability. If you are spending $5,000 on tokens, a $500 monthly bill for observability is reasonable. If the vendor price is higher, consider building custom logging into your existing SQL database or data warehouse.

What is the ideal P95 latency for a RAG system?

There is no single universal number; the right P95 target depends on your use case. For interactive, conversational applications, keeping responses under a few seconds is a sensible goal. Once latency climbs past the four second mark, user satisfaction tends to drop, as the interaction no longer feels like a conversation.

Should we use dbt for our AI data pipelines?

Yes. Using dbt ensures that your AI has a clean, documented, and governed data source. It allows your data engineers to apply the same quality tests to your AI inputs as they do to your executive dashboards. Without dbt or a similar transformation layer, your AI will eventually suffer from "garbage in, garbage out."

When should we move from a prototype to production?

You are ready for production when your automated evaluation scores are consistently above your target threshold (usually 85% to 90% accuracy) and you have implemented basic monitoring and cost controls. Do not wait for 100% accuracy, as LLMs are probabilistic and will never be perfect.

Ready to benchmark your AI stack?

Building a production grade system is a journey from "it works on my machine" to "it works for my customers." If you are unsure where your team stands, our AI Stack Audit provides a comprehensive assessment of your architecture, performance, and readiness. We help you identify the specific bottlenecks preventing you from shipping.

If you would rather talk through your architecture with a practitioner, you can book a free consultation to discuss your data foundation and production goals.