AI Engineering: Building Production-Ready LLM Applications

The AI Engineering Paradigm

We’re witnessing a fundamental shift in software development. Traditional programming requires specifying exact logic; AI engineering requires specifying outcomes and letting models figure out the implementation.

This guide covers building production-ready AI applications that are reliable, scalable, and maintainable.

Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                     User Interface                          │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│                   API Layer (FastAPI/Next.js)              │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐    │
│  │ Rate Limit  │  │   Auth      │  │  Cache (Redis)  │    │
│  └─────────────┘  └─────────────┘  └─────────────────┘    │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│                 Application Layer                           │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────┐    │
│  │   Prompt    │  │    Tool     │  │   Memory        │    │
│  │  Manager    │  │   Executor  │  │   Manager       │    │
│  └─────────────┘  └─────────────┘  └─────────────────┘    │
└─────────────────────┬───────────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────────┐
│                   Model Layer                               │
│  ┌─────────────────────────────────────────────────────┐    │
│  │          LLM (OpenAI/Anthropic/Custom)              │    │
│  └─────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────┘

1. Prompt Engineering Patterns

The RAG Pattern (Retrieval Augmented Generation)

interface RAGPipeline {
  // 1. Embed user query
  embedQuery(query: string): Promise<number[]>;

  // 2. Search vector database
  search(embedding: number[], k: number): Promise<Document[]>;

  // 3. Build context
  buildContext(docs: Document[]): string;

  // 4. Generate response
  generate(context: string, query: string): Promise<string>;
}

class ProductionRAG implements RAGPipeline {
  constructor(
    private embeddings: EmbeddingsClient,
    private vectorStore: VectorStore,
    private llm: LLMClient
  ) {}

  async query(userQuery: string): Promise<string> {
    const queryEmbedding = await this.embeddings.embed(userQuery);
    const relevantDocs = await this.vectorStore.similaritySearch(
      queryEmbedding,
      k: 5
    );

    const context = this.buildContext(relevantDocs);
    const systemPrompt = `You are a helpful assistant.
Use the following context to answer questions:

${context}`;

    return this.llm.chat([
      { role: 'system', content: systemPrompt },
      { role: 'user', content: userQuery }
    ]);
  }
}

Chain of Thought Reasoning

# Enable step-by-step reasoning
COT_PROMPT = """Solve this problem step by step. Show your reasoning.

Question: {question}

Let's think step by step:"""

def solve_with_cot(question: str) -> str:
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": COT_PROMPT},
            {"role": "user", "content": question}
        ],
        temperature=0.7,
        max_tokens=1000
    )
    return response.choices[0].message.content

2. Tool Use & Function Calling

Modern LLMs can use tools. Here’s how to implement it:

interface Tool {
  name: string;
  description: string;
  parameters: z.ZodSchema;
  execute: (args: any) => Promise<any>;
}

const tools: Tool[] = [
  {
    name: "search_codebase",
    description: "Search for code in the repository",
    parameters: z.object({
      query: z.string(),
      file_types: z.array(z.string()).optional()
    }),
    async execute({ query, file_types }) {
      return searchCode(query, file_types);
    }
  },
  {
    name: "run_tests",
    description: "Run test suite",
    parameters: z.object({
      pattern: z.string().optional(),
      coverage: z.boolean().default(false)
    }),
    async execute({ pattern, coverage }) {
      return runTestSuite({ pattern, coverage });
    }
  }
];

class ToolExecutor {
  async executeWithTools(prompt: string): Promise<string> {
    const response = await this.llm.chat([
      { role: 'user', content: prompt }
    ], {
      tools: tools.map(t => ({
        type: 'function',
        function: {
          name: t.name,
          description: t.description,
          parameters: t.parameters
        }
      })
    });

    const toolCalls = response.tool_calls;
    if (!toolCalls) return response.content;

    // Execute all tools in parallel
    const results = await Promise.all(
      toolCalls.map(call => {
        const tool = tools.find(t => t.name === call.function.name);
        const args = JSON.parse(call.function.arguments);
        return tool!.execute(args);
      })
    );

    // Feed results back to LLM
    return this.llm.chat([
      ...messages,
      ...toolCalls.map((call, i) => ({
        role: 'tool' as const,
        tool_call_id: call.id,
        content: JSON.stringify(results[i])
      })),
      { role: 'user', content: 'Continue with the results.' }
    ]);
  }
}

3. Memory Management

Conversation Context

class ConversationMemory {
  private messages: Message[] = [];
  private maxTokens: number;

  constructor(maxTokens: number = 4000) {
    this.maxTokens = maxTokens;
  }

  add(message: Message): void {
    this.messages.push(message);
    this.prune();
  }

  getMessages(): Message[] {
    return [...this.messages];
  }

  private prune(): void {
    let tokenCount = this.messages.reduce(
      (sum, m) => sum + estimateTokens(m.content), 0
    );

    while (tokenCount > this.maxTokens && this.messages.length > 1) {
      const removed = this.messages.shift();
      tokenCount -= estimateTokens(removed!.content);
    }
  }
}

Persistent Memory with Vector Store

class LongTermMemory {
  constructor(
    private vectorStore: VectorStore,
    private embeddings: EmbeddingsClient
  ) {}

  async remember(key: string, value: string): Promise<void> {
    const embedding = await this.embeddings.embed(value);
    await this.vectorStore.upsert({
      id: key,
      embedding,
      payload: { key, value, timestamp: Date.now() }
    });
  }

  async recall(query: string, topK: number = 3): Promise<string[]> {
    const queryEmbedding = await this.embeddings.embed(query);
    const results = await this.vectorStore.search(
      queryEmbedding,
      topK
    );
    return results.map(r => r.payload.value);
  }
}

4. Production Considerations

Rate Limiting & Cost Control

class RateLimitedClient {
  private queue: Array<() => Promise<any>> = [];
  private processing = false;

  constructor(
    private client: LLMClient,
    private maxPerMinute: number = 60,
    private maxPerDay: number = 10000
  ) {
    // Track usage
    this.usage = {
      minute: [],
      day: []
    };
  }

  async chat(messages: Message[]): Promise<Response> {
    this.checkLimits();

    const waitForSlot = async () => {
      while (this.usage.minute.length >= this.maxPerMinute) {
        await sleep(1000);
      }
    };

    await waitForSlot();

    const now = Date.now();
    this.usage.minute.push(now);
    this.usage.day.push(now);

    // Clean old entries
    this.usage.minute = this.usage.minute.filter(
      t => now - t < 60000
    );

    return this.client.chat(messages);
  }
}

Caching Strategies

class CachedLLMClient {
  private cache: Redis;

  async chat(messages: Message[]): Promise<Response> {
    const cacheKey = this.hashMessages(messages);

    const cached = await this.cache.get(cacheKey);
    if (cached) {
      return JSON.parse(cached);
    }

    const response = await this.client.chat(messages);

    // Cache successful responses for 1 hour
    await this.cache.setex(cacheKey, 3600, JSON.stringify(response));

    return response;
  }
}

5. Evaluation & Monitoring

A/B Testing Prompts

class PromptExperiment {
  private metrics: MetricClient;

  async runExperiment(
    experimentId: string,
    variants: Map<string, (input: Input) => Promise<Output>>
  ): Promise<ExperimentResult> {
    const trafficSplit = this.getTrafficSplit(experimentId);

    const results = await Promise.all(
      Array.from(variants.entries()).map(
        async ([variant, fn]) => {
          const inputs = this.getTestInputs(variant);
          const outputs = await Promise.all(
            inputs.map(fn)
          );
          return {
            variant,
            outputs,
            metrics: this.evaluate(outputs)
          };
        }
      )
    );

    return this.statisticalAnalysis(results);
  }
}

Key Metrics to Track

const METRICS = {
  // Quality metrics
  accuracy: 'Correct answers / Total questions',
  relevance: 'Response relevance to user query',
  coherence: 'Logical flow and consistency',

  // Performance metrics
  latency: 'Time to first token',
  throughput: 'Tokens per second',
  cost: 'Cost per 1K tokens',

  // Reliability metrics
  errorRate: 'Failed requests / Total requests',
  timeoutRate: 'Timeouts / Total requests',
  retryRate: 'Retries / Total requests'
};

Conclusion

Building production AI applications requires the same rigor as traditional software engineering—plus understanding of LLM behavior, prompt engineering, and new failure modes.

Key takeaways:

1. Start with RAG for knowledge-intensive tasks
2. Use tools to extend LLM capabilities
3. Implement proper memory for conversational apps
4. Always have fallback when LLMs fail
5. Measure everything in production

The future is AI-augmented software. Build it right.

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